{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Implementation of Boundary Equilibrium GANs\n",
    "Reference: https://arxiv.org/pdf/1703.10717"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Run the comment below only when using Google Colab\n",
    "# !pip install torch torchvision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "import torchvision\n",
    "import torch.nn as nn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from torch.utils.data import DataLoader\n",
    "from torch.utils.data.dataset import Dataset\n",
    "from torchvision import datasets\n",
    "from torchvision import transforms\n",
    "from torchvision.utils import save_image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import datetime\n",
    "import os, sys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import glob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from PIL import Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.pyplot import imshow, imsave\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "MODEL_NAME = 'BEGAN'\n",
    "DEVICE = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "IMAGE_DIM = (64, 64, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def tensor2img(tensor):\n",
    "    img = (np.transpose(tensor.detach().cpu().numpy(), [1,2,0])+1)/2.\n",
    "    return img"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_sample_image(G, n_noise=100, n_samples=64):\n",
    "    \"\"\"\n",
    "        save sample 100 images\n",
    "    \"\"\"\n",
    "    n_rows = int(np.sqrt(n_samples))\n",
    "    z = (torch.rand(size=[n_samples, n_noise])*2-1).to(DEVICE) # U[-1, 1]\n",
    "    x_fake = G(z)\n",
    "    x_fake = torch.cat([torch.cat([x_fake[n_rows*j+i] for i in range(n_rows)], dim=1) for j in range(n_rows)], dim=2)\n",
    "    result = tensor2img(x_fake)\n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class Encoder(nn.Module):\n",
    "    \"\"\"\n",
    "        Convolutional Encoder\n",
    "    \"\"\"\n",
    "    def __init__(self, in_channel=1, n_filters=128, hidden_dim=100):\n",
    "        super(Encoder, self).__init__()\n",
    "        assert IMAGE_DIM[0] % 2**2 == 0, 'Should be divided 4'\n",
    "        self.flatten_dim = IMAGE_DIM[0]//2**2\n",
    "        self.conv = nn.Sequential(\n",
    "            # conv0\n",
    "            nn.Conv2d(in_channel, n_filters, 3, stride=1, padding=1, bias=False),\n",
    "            nn.BatchNorm2d(n_filters),\n",
    "            nn.LeakyReLU(0.2),\n",
    "            # conv1\n",
    "            nn.Conv2d(n_filters, n_filters, 3, stride=1, padding=1, bias=False),\n",
    "            nn.BatchNorm2d(n_filters),\n",
    "            nn.LeakyReLU(0.2),\n",
    "            # conv2\n",
    "            nn.Conv2d(n_filters, n_filters*2, 3, stride=2, padding=1, bias=False),\n",
    "            nn.BatchNorm2d(n_filters*2),\n",
    "            nn.LeakyReLU(0.2),\n",
    "            # conv3\n",
    "            nn.Conv2d(n_filters*2, n_filters*2, 3, stride=1, padding=1, bias=False),\n",
    "            nn.BatchNorm2d(n_filters*2),\n",
    "            nn.LeakyReLU(0.2),\n",
    "            # conv4\n",
    "            nn.Conv2d(n_filters*2, n_filters*3, 3, stride=2, padding=1, bias=False),\n",
    "            nn.BatchNorm2d(n_filters*3),\n",
    "            nn.LeakyReLU(0.2),\n",
    "            # conv5\n",
    "            nn.Conv2d(n_filters*3, n_filters*3, 3, stride=1, padding=1, bias=False),\n",
    "            nn.BatchNorm2d(n_filters*3),\n",
    "            nn.LeakyReLU(0.2),\n",
    "            # conv6\n",
    "            nn.Conv2d(n_filters*3, n_filters*3, 3, stride=1, padding=1, bias=False),\n",
    "            nn.BatchNorm2d(n_filters*3),\n",
    "            nn.LeakyReLU(0.2),\n",
    "#             nn.AdaptiveAvgPool2d(1),\n",
    "        )\n",
    "        self.fc = nn.Linear(self.flatten_dim**2*n_filters*3, hidden_dim)\n",
    "    \n",
    "    def forward(self, x):\n",
    "        h = self.conv(x)\n",
    "        h = h.view(h.size(0), -1)\n",
    "        h = self.fc(h)\n",
    "        return h"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class Decoder(nn.Module):\n",
    "    \"\"\"\n",
    "        Convolutional Decoder\n",
    "    \"\"\"\n",
    "    def __init__(self, out_channel=1, n_filters=128, n_noise=100):\n",
    "        super(Decoder, self).__init__()\n",
    "        assert IMAGE_DIM[0] % 2**2 == 0, 'Should be divided 4'\n",
    "        self.flatten_dim = IMAGE_DIM[0]//2**2\n",
    "        self.fc = nn.Sequential(\n",
    "            nn.Linear(n_noise, self.flatten_dim**2*n_filters),\n",
    "        )\n",
    "        self.conv = nn.Sequential(\n",
    "            # conv1\n",
    "            nn.Conv2d(n_filters, n_filters, 3, stride=1, padding=1, bias=False),\n",
    "            nn.BatchNorm2d(n_filters),\n",
    "            nn.LeakyReLU(0.2),\n",
    "            # conv2\n",
    "            nn.Conv2d(n_filters, n_filters, 3, stride=1, padding=1, bias=False),\n",
    "            nn.BatchNorm2d(n_filters),\n",
    "            nn.LeakyReLU(0.2),\n",
    "            nn.Upsample(scale_factor=2, mode='nearest'),\n",
    "            # conv3\n",
    "            nn.Conv2d(n_filters, n_filters, 3, stride=1, padding=1, bias=False),\n",
    "            nn.BatchNorm2d(n_filters),\n",
    "            nn.LeakyReLU(0.2),\n",
    "            # conv4\n",
    "            nn.Conv2d(n_filters, n_filters, 3, stride=1, padding=1, bias=False),\n",
    "            nn.BatchNorm2d(n_filters),\n",
    "            nn.LeakyReLU(0.2),\n",
    "            nn.Upsample(scale_factor=2, mode='nearest'),\n",
    "            # conv5\n",
    "            nn.Conv2d(n_filters, n_filters, 3, stride=1, padding=1, bias=False),\n",
    "            nn.BatchNorm2d(n_filters),\n",
    "            nn.LeakyReLU(0.2),\n",
    "            # conv6\n",
    "            nn.Conv2d(n_filters, n_filters, 3, stride=1, padding=1, bias=False),\n",
    "            nn.BatchNorm2d(n_filters),\n",
    "            nn.LeakyReLU(0.2),\n",
    "            # conv6\n",
    "            nn.Conv2d(n_filters, out_channel, 3, stride=1, padding=1, bias=True),\n",
    "            nn.Tanh()\n",
    "        )\n",
    "    \n",
    "    def forward(self, h):\n",
    "        h = self.fc(h)\n",
    "        h = h.view(h.size(0), -1, self.flatten_dim, self.flatten_dim)\n",
    "        x = self.conv(h)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class Discriminator(nn.Module):\n",
    "    \"\"\"\n",
    "        Convolutional Discriminator\n",
    "    \"\"\"\n",
    "    def __init__(self, in_channel=1, n_filters=128, hidden_dim=64):\n",
    "        super(Discriminator, self).__init__()\n",
    "        self.encoder = Encoder(in_channel=in_channel, n_filters=n_filters, hidden_dim=hidden_dim)\n",
    "        self.decoder = Decoder(out_channel=in_channel, n_filters=n_filters, n_noise=hidden_dim)\n",
    "        \n",
    "    def forward(self, x):\n",
    "        h = self.encoder(x)\n",
    "        x_ = self.decoder(h)\n",
    "        return x_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class Generator(nn.Module):\n",
    "    \"\"\"\n",
    "        Convolutional Generator\n",
    "    \"\"\"\n",
    "    def __init__(self, out_channel=1, n_filters=128, n_noise=64):\n",
    "        super(Generator, self).__init__()\n",
    "        self.decoder = Decoder(out_channel=out_channel, n_filters=n_filters, n_noise=n_noise)\n",
    "        \n",
    "    def forward(self, h):\n",
    "        x_ = self.decoder(h)\n",
    "        return x_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "transform = transforms.Compose([transforms.Resize((IMAGE_DIM[0],IMAGE_DIM[1])),\n",
    "                                transforms.ToTensor(),\n",
    "                                transforms.Normalize(mean=(0.5, 0.5, 0.5),\n",
    "                                std=(0.5, 0.5, 0.5))\n",
    "                               ]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "CelebA-aligned download: [link](http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dataset = datasets.ImageFolder(root='/home/yangyangii/gans/CelebA/dataset', transform=transform)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "batch_size = 32\n",
    "n_noise = 64"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_loader = DataLoader(dataset=dataset, batch_size=batch_size, shuffle=True, drop_last=True, num_workers=8, pin_memory=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "D = Discriminator(in_channel=IMAGE_DIM[-1], hidden_dim=n_noise).to(DEVICE)\n",
    "G = Generator(out_channel=IMAGE_DIM[-1], n_noise=n_noise).to(DEVICE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "D_opt = torch.optim.Adam(D.parameters(), lr=0.0002, betas=(0.5, 0.999))\n",
    "G_opt = torch.optim.Adam(G.parameters(), lr=0.0002, betas=(0.5, 0.999))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# BEGAN causes mode collapse. it can be addressed by decaying lr\n",
    "D_scheduler = torch.optim.lr_scheduler.MultiStepLR(D_opt, milestones=[3, 10, 17], gamma=0.6)\n",
    "G_scheduler = torch.optim.lr_scheduler.MultiStepLR(G_opt, milestones=[3, 10, 17], gamma=0.6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "criterion = nn.L1Loss()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "max_epoch = 20\n",
    "step = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "lr_k = 0.001\n",
    "gamma = 0.7\n",
    "k_t = 0\n",
    "log_term = 1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6331"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "steps_per_epoch = len(data_loader.dataset) // batch_size\n",
    "steps_per_epoch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "if not os.path.exists('samples'):\n",
    "    os.makedirs('samples')\n",
    "    \n",
    "if not os.path.exists('ckpt'):\n",
    "    os.makedirs('ckpt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def save_checkpoint(state, file_name='checkpoint.pth.tar'):\n",
    "    torch.save(state, file_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/yangyangii/anaconda3/lib/python3.6/site-packages/torch/nn/modules/upsampling.py:129: UserWarning: nn.Upsample is deprecated. Use nn.functional.interpolate instead.\n",
      "  warnings.warn(\"nn.{} is deprecated. Use nn.functional.interpolate instead.\".format(self.name))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0/20, Step: 0, D Loss: 0.5115, G Loss: 0.2565, k: 0.0001, M: 0.6131, lr: 0.0002000, Time:20:18:28\n",
      "Epoch: 0/20, Step: 1000, D Loss: 0.1368, G Loss: 0.0665, k: 0.0341, M: 0.1698, lr: 0.0002000, Time:20:25:16\n",
      "Epoch: 0/20, Step: 2000, D Loss: 0.1480, G Loss: 0.0705, k: 0.0655, M: 0.1882, lr: 0.0002000, Time:20:32:00\n",
      "Epoch: 0/20, Step: 3000, D Loss: 0.1428, G Loss: 0.0965, k: 0.0797, M: 0.1571, lr: 0.0002000, Time:20:38:42\n",
      "Epoch: 0/20, Step: 4000, D Loss: 0.1348, G Loss: 0.0875, k: 0.0853, M: 0.1536, lr: 0.0002000, Time:20:45:25\n",
      "Epoch: 0/20, Step: 5000, D Loss: 0.1275, G Loss: 0.0940, k: 0.0894, M: 0.1356, lr: 0.0002000, Time:20:52:07\n",
      "Epoch: 0/20, Step: 6000, D Loss: 0.1070, G Loss: 0.0790, k: 0.0908, M: 0.1158, lr: 0.0002000, Time:20:58:50\n",
      "Epoch: 1/20, Step: 7000, D Loss: 0.1168, G Loss: 0.0831, k: 0.0905, M: 0.1292, lr: 0.0002000, Time:21:05:33\n",
      "Epoch: 1/20, Step: 8000, D Loss: 0.1191, G Loss: 0.0991, k: 0.0883, M: 0.1375, lr: 0.0002000, Time:21:12:16\n",
      "Epoch: 1/20, Step: 9000, D Loss: 0.1146, G Loss: 0.0961, k: 0.0865, M: 0.1328, lr: 0.0002000, Time:21:18:59\n",
      "Epoch: 1/20, Step: 10000, D Loss: 0.1066, G Loss: 0.0798, k: 0.0836, M: 0.1140, lr: 0.0002000, Time:21:25:43\n",
      "Epoch: 1/20, Step: 11000, D Loss: 0.1159, G Loss: 0.0886, k: 0.0810, M: 0.1254, lr: 0.0002000, Time:21:32:25\n",
      "Epoch: 1/20, Step: 12000, D Loss: 0.1127, G Loss: 0.0849, k: 0.0787, M: 0.1209, lr: 0.0002000, Time:21:39:08\n",
      "Epoch: 2/20, Step: 13000, D Loss: 0.1155, G Loss: 0.0930, k: 0.0761, M: 0.1296, lr: 0.0002000, Time:21:45:52\n",
      "Epoch: 2/20, Step: 14000, D Loss: 0.1119, G Loss: 0.0874, k: 0.0735, M: 0.1228, lr: 0.0002000, Time:21:52:34\n",
      "Epoch: 2/20, Step: 15000, D Loss: 0.1138, G Loss: 0.0874, k: 0.0716, M: 0.1234, lr: 0.0002000, Time:21:59:18\n",
      "Epoch: 2/20, Step: 16000, D Loss: 0.1065, G Loss: 0.0881, k: 0.0698, M: 0.1220, lr: 0.0002000, Time:22:06:02\n",
      "Epoch: 2/20, Step: 17000, D Loss: 0.1099, G Loss: 0.0841, k: 0.0688, M: 0.1187, lr: 0.0002000, Time:22:12:47\n",
      "Epoch: 2/20, Step: 18000, D Loss: 0.1117, G Loss: 0.0893, k: 0.0677, M: 0.1246, lr: 0.0002000, Time:22:19:31\n",
      "Epoch: 3/20, Step: 19000, D Loss: 0.1067, G Loss: 0.0825, k: 0.0661, M: 0.1162, lr: 0.0002000, Time:22:26:15\n",
      "Epoch: 3/20, Step: 20000, D Loss: 0.1113, G Loss: 0.0795, k: 0.0645, M: 0.1184, lr: 0.0002000, Time:22:33:00\n",
      "Epoch: 3/20, Step: 21000, D Loss: 0.1063, G Loss: 0.0774, k: 0.0638, M: 0.1114, lr: 0.0002000, Time:22:39:46\n",
      "Epoch: 3/20, Step: 22000, D Loss: 0.1034, G Loss: 0.0801, k: 0.0632, M: 0.1126, lr: 0.0002000, Time:22:46:33\n",
      "Epoch: 3/20, Step: 23000, D Loss: 0.1116, G Loss: 0.0854, k: 0.0625, M: 0.1205, lr: 0.0002000, Time:22:53:18\n",
      "Epoch: 3/20, Step: 24000, D Loss: 0.1064, G Loss: 0.0759, k: 0.0618, M: 0.1128, lr: 0.0002000, Time:23:00:06\n",
      "Epoch: 3/20, Step: 25000, D Loss: 0.1025, G Loss: 0.0813, k: 0.0614, M: 0.1135, lr: 0.0002000, Time:23:06:51\n",
      "Epoch: 4/20, Step: 26000, D Loss: 0.1174, G Loss: 0.0768, k: 0.0611, M: 0.1307, lr: 0.0001200, Time:23:13:38\n",
      "Epoch: 4/20, Step: 27000, D Loss: 0.0972, G Loss: 0.0739, k: 0.0607, M: 0.1044, lr: 0.0001200, Time:23:20:26\n",
      "Epoch: 4/20, Step: 28000, D Loss: 0.1031, G Loss: 0.0793, k: 0.0597, M: 0.1116, lr: 0.0001200, Time:23:27:15\n",
      "Epoch: 4/20, Step: 29000, D Loss: 0.1015, G Loss: 0.0713, k: 0.0591, M: 0.1086, lr: 0.0001200, Time:23:34:03\n",
      "Epoch: 4/20, Step: 30000, D Loss: 0.0988, G Loss: 0.0688, k: 0.0582, M: 0.1057, lr: 0.0001200, Time:23:40:52\n",
      "Epoch: 4/20, Step: 31000, D Loss: 0.0979, G Loss: 0.0751, k: 0.0576, M: 0.1057, lr: 0.0001200, Time:23:47:41\n",
      "Epoch: 5/20, Step: 32000, D Loss: 0.1089, G Loss: 0.0830, k: 0.0574, M: 0.1171, lr: 0.0001200, Time:23:54:30\n",
      "Epoch: 5/20, Step: 33000, D Loss: 0.1104, G Loss: 0.0754, k: 0.0567, M: 0.1196, lr: 0.0001200, Time:00:01:19\n",
      "Epoch: 5/20, Step: 34000, D Loss: 0.1068, G Loss: 0.0720, k: 0.0560, M: 0.1161, lr: 0.0001200, Time:00:08:08\n",
      "Epoch: 5/20, Step: 35000, D Loss: 0.1096, G Loss: 0.0797, k: 0.0551, M: 0.1138, lr: 0.0001200, Time:00:14:57\n",
      "Epoch: 5/20, Step: 36000, D Loss: 0.0973, G Loss: 0.0748, k: 0.0539, M: 0.1052, lr: 0.0001200, Time:00:21:45\n",
      "Epoch: 5/20, Step: 37000, D Loss: 0.0935, G Loss: 0.0668, k: 0.0534, M: 0.0981, lr: 0.0001200, Time:00:28:35\n",
      "Epoch: 6/20, Step: 38000, D Loss: 0.1154, G Loss: 0.0792, k: 0.0529, M: 0.1238, lr: 0.0001200, Time:00:35:24\n",
      "Epoch: 6/20, Step: 39000, D Loss: 0.1113, G Loss: 0.0692, k: 0.0524, M: 0.1262, lr: 0.0001200, Time:00:42:13\n",
      "Epoch: 6/20, Step: 40000, D Loss: 0.1069, G Loss: 0.0775, k: 0.0512, M: 0.1108, lr: 0.0001200, Time:00:49:02\n",
      "Epoch: 6/20, Step: 41000, D Loss: 0.1056, G Loss: 0.0756, k: 0.0505, M: 0.1100, lr: 0.0001200, Time:00:55:51\n",
      "Epoch: 6/20, Step: 42000, D Loss: 0.1036, G Loss: 0.0748, k: 0.0510, M: 0.1077, lr: 0.0001200, Time:01:02:41\n",
      "Epoch: 6/20, Step: 43000, D Loss: 0.1079, G Loss: 0.0782, k: 0.0500, M: 0.1118, lr: 0.0001200, Time:01:09:30\n",
      "Epoch: 6/20, Step: 44000, D Loss: 0.1074, G Loss: 0.0716, k: 0.0497, M: 0.1170, lr: 0.0001200, Time:01:16:19\n",
      "Epoch: 7/20, Step: 45000, D Loss: 0.1001, G Loss: 0.0751, k: 0.0491, M: 0.1062, lr: 0.0001200, Time:01:23:09\n",
      "Epoch: 7/20, Step: 46000, D Loss: 0.1056, G Loss: 0.0645, k: 0.0491, M: 0.1203, lr: 0.0001200, Time:01:29:58\n",
      "Epoch: 7/20, Step: 47000, D Loss: 0.1032, G Loss: 0.0775, k: 0.0487, M: 0.1096, lr: 0.0001200, Time:01:36:47\n",
      "Epoch: 7/20, Step: 48000, D Loss: 0.1020, G Loss: 0.0782, k: 0.0483, M: 0.1099, lr: 0.0001200, Time:01:43:37\n",
      "Epoch: 7/20, Step: 49000, D Loss: 0.1011, G Loss: 0.0707, k: 0.0485, M: 0.1069, lr: 0.0001200, Time:01:50:26\n",
      "Epoch: 7/20, Step: 50000, D Loss: 0.1024, G Loss: 0.0744, k: 0.0487, M: 0.1062, lr: 0.0001200, Time:01:57:16\n",
      "Epoch: 8/20, Step: 51000, D Loss: 0.1032, G Loss: 0.0807, k: 0.0483, M: 0.1128, lr: 0.0001200, Time:02:04:06\n",
      "Epoch: 8/20, Step: 52000, D Loss: 0.0983, G Loss: 0.0682, k: 0.0475, M: 0.1048, lr: 0.0001200, Time:02:10:55\n",
      "Epoch: 8/20, Step: 53000, D Loss: 0.1073, G Loss: 0.0797, k: 0.0472, M: 0.1130, lr: 0.0001200, Time:02:17:44\n",
      "Epoch: 8/20, Step: 54000, D Loss: 0.1050, G Loss: 0.0747, k: 0.0467, M: 0.1096, lr: 0.0001200, Time:02:24:34\n",
      "Epoch: 8/20, Step: 55000, D Loss: 0.1038, G Loss: 0.0738, k: 0.0463, M: 0.1084, lr: 0.0001200, Time:02:31:23\n",
      "Epoch: 8/20, Step: 56000, D Loss: 0.1016, G Loss: 0.0747, k: 0.0458, M: 0.1062, lr: 0.0001200, Time:02:38:12\n",
      "Epoch: 9/20, Step: 57000, D Loss: 0.1116, G Loss: 0.0732, k: 0.0453, M: 0.1222, lr: 0.0001200, Time:02:45:02\n",
      "Epoch: 9/20, Step: 58000, D Loss: 0.1041, G Loss: 0.0748, k: 0.0446, M: 0.1079, lr: 0.0001200, Time:02:51:51\n",
      "Epoch: 9/20, Step: 59000, D Loss: 0.1072, G Loss: 0.0730, k: 0.0446, M: 0.1146, lr: 0.0001200, Time:02:58:40\n",
      "Epoch: 9/20, Step: 60000, D Loss: 0.0994, G Loss: 0.0741, k: 0.0441, M: 0.1049, lr: 0.0001200, Time:03:05:30\n",
      "Epoch: 9/20, Step: 61000, D Loss: 0.1120, G Loss: 0.0717, k: 0.0440, M: 0.1239, lr: 0.0001200, Time:03:12:19\n",
      "Epoch: 9/20, Step: 62000, D Loss: 0.1033, G Loss: 0.0744, k: 0.0440, M: 0.1067, lr: 0.0001200, Time:03:19:08\n",
      "Epoch: 9/20, Step: 63000, D Loss: 0.1067, G Loss: 0.0729, k: 0.0438, M: 0.1139, lr: 0.0001200, Time:03:25:56\n",
      "Epoch: 10/20, Step: 64000, D Loss: 0.1023, G Loss: 0.0738, k: 0.0437, M: 0.1056, lr: 0.0001200, Time:03:32:46\n",
      "Epoch: 10/20, Step: 65000, D Loss: 0.1020, G Loss: 0.0756, k: 0.0430, M: 0.1071, lr: 0.0001200, Time:03:39:35\n",
      "Epoch: 10/20, Step: 66000, D Loss: 0.0958, G Loss: 0.0740, k: 0.0426, M: 0.1037, lr: 0.0001200, Time:03:46:23\n",
      "Epoch: 10/20, Step: 67000, D Loss: 0.1034, G Loss: 0.0766, k: 0.0427, M: 0.1085, lr: 0.0001200, Time:03:53:13\n",
      "Epoch: 10/20, Step: 68000, D Loss: 0.1029, G Loss: 0.0793, k: 0.0429, M: 0.1112, lr: 0.0001200, Time:04:00:02\n",
      "Epoch: 10/20, Step: 69000, D Loss: 0.1040, G Loss: 0.0741, k: 0.0425, M: 0.1081, lr: 0.0001200, Time:04:06:50\n",
      "Epoch: 11/20, Step: 70000, D Loss: 0.1018, G Loss: 0.0750, k: 0.0421, M: 0.1065, lr: 0.0000720, Time:04:13:39\n",
      "Epoch: 11/20, Step: 71000, D Loss: 0.1012, G Loss: 0.0729, k: 0.0412, M: 0.1043, lr: 0.0000720, Time:04:20:27\n",
      "Epoch: 11/20, Step: 72000, D Loss: 0.1010, G Loss: 0.0784, k: 0.0409, M: 0.1096, lr: 0.0000720, Time:04:27:16\n",
      "Epoch: 11/20, Step: 73000, D Loss: 0.1024, G Loss: 0.0725, k: 0.0405, M: 0.1067, lr: 0.0000720, Time:04:34:05\n",
      "Epoch: 11/20, Step: 74000, D Loss: 0.1035, G Loss: 0.0758, k: 0.0398, M: 0.1077, lr: 0.0000720, Time:04:40:53\n",
      "Epoch: 11/20, Step: 75000, D Loss: 0.1028, G Loss: 0.0757, k: 0.0394, M: 0.1074, lr: 0.0000720, Time:04:47:42\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 12/20, Step: 76000, D Loss: 0.1038, G Loss: 0.0722, k: 0.0391, M: 0.1091, lr: 0.0000720, Time:04:54:32\n",
      "Epoch: 12/20, Step: 77000, D Loss: 0.1000, G Loss: 0.0767, k: 0.0383, M: 0.1076, lr: 0.0000720, Time:05:01:21\n",
      "Epoch: 12/20, Step: 78000, D Loss: 0.0993, G Loss: 0.0734, k: 0.0376, M: 0.1040, lr: 0.0000720, Time:05:08:10\n",
      "Epoch: 12/20, Step: 79000, D Loss: 0.0974, G Loss: 0.0718, k: 0.0374, M: 0.1019, lr: 0.0000720, Time:05:14:58\n",
      "Epoch: 12/20, Step: 80000, D Loss: 0.0999, G Loss: 0.0703, k: 0.0373, M: 0.1042, lr: 0.0000720, Time:05:21:47\n",
      "Epoch: 12/20, Step: 81000, D Loss: 0.0949, G Loss: 0.0721, k: 0.0373, M: 0.1014, lr: 0.0000720, Time:05:28:36\n",
      "Epoch: 12/20, Step: 82000, D Loss: 0.1020, G Loss: 0.0761, k: 0.0367, M: 0.1075, lr: 0.0000720, Time:05:35:25\n",
      "Epoch: 13/20, Step: 83000, D Loss: 0.1046, G Loss: 0.0730, k: 0.0363, M: 0.1093, lr: 0.0000720, Time:05:42:14\n",
      "Epoch: 13/20, Step: 84000, D Loss: 0.0937, G Loss: 0.0726, k: 0.0360, M: 0.1015, lr: 0.0000720, Time:05:49:03\n",
      "Epoch: 13/20, Step: 85000, D Loss: 0.1024, G Loss: 0.0750, k: 0.0356, M: 0.1065, lr: 0.0000720, Time:05:55:52\n",
      "Epoch: 13/20, Step: 86000, D Loss: 0.1059, G Loss: 0.0730, k: 0.0355, M: 0.1113, lr: 0.0000720, Time:06:02:41\n",
      "Epoch: 13/20, Step: 87000, D Loss: 0.1045, G Loss: 0.0756, k: 0.0351, M: 0.1077, lr: 0.0000720, Time:06:09:30\n",
      "Epoch: 13/20, Step: 88000, D Loss: 0.0959, G Loss: 0.0699, k: 0.0354, M: 0.0994, lr: 0.0000720, Time:06:16:20\n",
      "Epoch: 14/20, Step: 89000, D Loss: 0.0934, G Loss: 0.0723, k: 0.0351, M: 0.1010, lr: 0.0000720, Time:06:23:09\n",
      "Epoch: 14/20, Step: 90000, D Loss: 0.1032, G Loss: 0.0769, k: 0.0348, M: 0.1087, lr: 0.0000720, Time:06:29:59\n",
      "Epoch: 14/20, Step: 91000, D Loss: 0.0949, G Loss: 0.0746, k: 0.0346, M: 0.1038, lr: 0.0000720, Time:06:36:48\n",
      "Epoch: 14/20, Step: 92000, D Loss: 0.0964, G Loss: 0.0758, k: 0.0344, M: 0.1055, lr: 0.0000720, Time:06:43:38\n",
      "Epoch: 14/20, Step: 93000, D Loss: 0.0978, G Loss: 0.0743, k: 0.0344, M: 0.1045, lr: 0.0000720, Time:06:50:27\n",
      "Epoch: 14/20, Step: 94000, D Loss: 0.1078, G Loss: 0.0698, k: 0.0342, M: 0.1176, lr: 0.0000720, Time:06:57:17\n",
      "Epoch: 15/20, Step: 95000, D Loss: 0.0942, G Loss: 0.0721, k: 0.0342, M: 0.1011, lr: 0.0000720, Time:07:04:07\n",
      "Epoch: 15/20, Step: 96000, D Loss: 0.0994, G Loss: 0.0713, k: 0.0343, M: 0.1018, lr: 0.0000720, Time:07:10:56\n",
      "Epoch: 15/20, Step: 97000, D Loss: 0.0994, G Loss: 0.0743, k: 0.0341, M: 0.1049, lr: 0.0000720, Time:07:17:46\n",
      "Epoch: 15/20, Step: 98000, D Loss: 0.0961, G Loss: 0.0732, k: 0.0342, M: 0.1027, lr: 0.0000720, Time:07:24:35\n",
      "Epoch: 15/20, Step: 99000, D Loss: 0.0983, G Loss: 0.0708, k: 0.0340, M: 0.1010, lr: 0.0000720, Time:07:31:25\n",
      "Epoch: 15/20, Step: 100000, D Loss: 0.0990, G Loss: 0.0742, k: 0.0341, M: 0.1046, lr: 0.0000720, Time:07:38:14\n",
      "Epoch: 15/20, Step: 101000, D Loss: 0.1019, G Loss: 0.0761, k: 0.0340, M: 0.1075, lr: 0.0000720, Time:07:45:03\n",
      "Epoch: 16/20, Step: 102000, D Loss: 0.0988, G Loss: 0.0797, k: 0.0336, M: 0.1101, lr: 0.0000720, Time:07:51:52\n",
      "Epoch: 16/20, Step: 103000, D Loss: 0.0976, G Loss: 0.0718, k: 0.0332, M: 0.1018, lr: 0.0000720, Time:07:58:42\n",
      "Epoch: 16/20, Step: 104000, D Loss: 0.0969, G Loss: 0.0701, k: 0.0332, M: 0.0999, lr: 0.0000720, Time:08:05:31\n",
      "Epoch: 16/20, Step: 105000, D Loss: 0.0952, G Loss: 0.0658, k: 0.0326, M: 0.0995, lr: 0.0000720, Time:08:12:20\n",
      "Epoch: 16/20, Step: 106000, D Loss: 0.1007, G Loss: 0.0640, k: 0.0325, M: 0.1107, lr: 0.0000720, Time:08:19:10\n",
      "Epoch: 16/20, Step: 107000, D Loss: 0.0944, G Loss: 0.0703, k: 0.0328, M: 0.0993, lr: 0.0000720, Time:08:25:59\n",
      "Epoch: 17/20, Step: 108000, D Loss: 0.0979, G Loss: 0.0736, k: 0.0334, M: 0.1037, lr: 0.0000720, Time:08:32:49\n",
      "Epoch: 17/20, Step: 109000, D Loss: 0.1082, G Loss: 0.0783, k: 0.0331, M: 0.1115, lr: 0.0000720, Time:08:39:38\n",
      "Epoch: 17/20, Step: 110000, D Loss: 0.1034, G Loss: 0.0648, k: 0.0331, M: 0.1146, lr: 0.0000720, Time:08:46:28\n",
      "Epoch: 17/20, Step: 111000, D Loss: 0.1027, G Loss: 0.0725, k: 0.0332, M: 0.1062, lr: 0.0000720, Time:08:53:18\n",
      "Epoch: 17/20, Step: 112000, D Loss: 0.0982, G Loss: 0.0729, k: 0.0329, M: 0.1030, lr: 0.0000720, Time:09:00:07\n",
      "Epoch: 17/20, Step: 113000, D Loss: 0.1004, G Loss: 0.0728, k: 0.0330, M: 0.1036, lr: 0.0000720, Time:09:06:57\n",
      "Epoch: 18/20, Step: 114000, D Loss: 0.0928, G Loss: 0.0719, k: 0.0328, M: 0.1004, lr: 0.0000432, Time:09:13:47\n",
      "Epoch: 18/20, Step: 115000, D Loss: 0.1011, G Loss: 0.0729, k: 0.0324, M: 0.1039, lr: 0.0000432, Time:09:20:36\n",
      "Epoch: 18/20, Step: 116000, D Loss: 0.1069, G Loss: 0.0710, k: 0.0320, M: 0.1146, lr: 0.0000432, Time:09:27:25\n",
      "Epoch: 18/20, Step: 117000, D Loss: 0.0980, G Loss: 0.0676, k: 0.0317, M: 0.1027, lr: 0.0000432, Time:09:34:15\n",
      "Epoch: 18/20, Step: 118000, D Loss: 0.0912, G Loss: 0.0701, k: 0.0315, M: 0.0981, lr: 0.0000432, Time:09:41:03\n",
      "Epoch: 18/20, Step: 119000, D Loss: 0.1033, G Loss: 0.0696, k: 0.0315, M: 0.1097, lr: 0.0000432, Time:09:47:52\n",
      "Epoch: 18/20, Step: 120000, D Loss: 0.0915, G Loss: 0.0718, k: 0.0310, M: 0.0999, lr: 0.0000432, Time:09:54:42\n",
      "Epoch: 19/20, Step: 121000, D Loss: 0.0886, G Loss: 0.0677, k: 0.0306, M: 0.0949, lr: 0.0000432, Time:10:01:31\n",
      "Epoch: 19/20, Step: 122000, D Loss: 0.0937, G Loss: 0.0699, k: 0.0303, M: 0.0986, lr: 0.0000432, Time:10:08:21\n",
      "Epoch: 19/20, Step: 123000, D Loss: 0.0937, G Loss: 0.0712, k: 0.0299, M: 0.1000, lr: 0.0000432, Time:10:15:10\n",
      "Epoch: 19/20, Step: 124000, D Loss: 0.0973, G Loss: 0.0700, k: 0.0297, M: 0.0999, lr: 0.0000432, Time:10:22:00\n",
      "Epoch: 19/20, Step: 125000, D Loss: 0.0965, G Loss: 0.0722, k: 0.0296, M: 0.1017, lr: 0.0000432, Time:10:28:49\n",
      "Epoch: 19/20, Step: 126000, D Loss: 0.1057, G Loss: 0.0672, k: 0.0298, M: 0.1159, lr: 0.0000432, Time:10:35:39\n"
     ]
    }
   ],
   "source": [
    "m_lst = []\n",
    "bestM = 1.\n",
    "for epoch in range(max_epoch):\n",
    "    for idx, (images, labels) in enumerate(data_loader):\n",
    "        G.zero_grad()\n",
    "        # Training Discriminator\n",
    "        x = images.to(DEVICE)\n",
    "        x_outputs = D(x)\n",
    "        D_x_loss = criterion(x_outputs, x)\n",
    "\n",
    "        z = (torch.rand(size=[batch_size, n_noise])*2-1).to(DEVICE)\n",
    "        x_fake = G(z)\n",
    "        z_outputs = D(x_fake.detach())\n",
    "        D_z_loss = criterion(z_outputs, x_fake)\n",
    "        \n",
    "        D_loss = D_x_loss - k_t*D_z_loss\n",
    "        \n",
    "        D.zero_grad()\n",
    "        D_loss.backward()\n",
    "        D_opt.step()\n",
    "\n",
    "        # Training Generator\n",
    "        z = (torch.rand(size=[batch_size, n_noise])*2-1).to(DEVICE)\n",
    "        x_fake = G(z)\n",
    "        z_outputs = D(x_fake)\n",
    "        G_loss = criterion(x_fake, z_outputs)\n",
    "\n",
    "        G.zero_grad()\n",
    "        G_loss.backward()\n",
    "        G_opt.step()\n",
    "        \n",
    "        bal = (gamma*D_x_loss - G_loss).detach()\n",
    "        k_t = min(max(k_t + lr_k*bal, 0), 1)\n",
    "        M_global = D_x_loss.detach() + torch.abs(bal)\n",
    "        \n",
    "        if M_global.item() < bestM:\n",
    "            bestM = M_global.item()\n",
    "            save_checkpoint({'global_step': step,\n",
    "                 'D':D.state_dict(),\n",
    "                 'G':G.state_dict(),\n",
    "                 'd_optim': D_opt.state_dict(),\n",
    "                 'g_optim' : G_opt.state_dict()},\n",
    "                'ckpt/began{:06d}.pth.tar'.format(step))\n",
    "        \n",
    "        if step % log_term == 0:\n",
    "            m_lst.append(M_global)\n",
    "            dt = datetime.datetime.now().strftime('%H:%M:%S')\n",
    "            print('Epoch: {}/{}, Step: {}, D Loss: {:.4f}, G Loss: {:.4f}, k: {:.4f}, M: {:.4f}, lr: {:.7f}, Time:{}'.format(epoch, max_epoch, step, D_loss.item(), G_loss.item(), k_t, M_global.item(), G_scheduler.get_lr()[0], dt))\n",
    "            G.eval()\n",
    "            img = get_sample_image(G, n_noise, n_samples=25)\n",
    "            imsave('samples/{}_step{:06d}.jpg'.format(MODEL_NAME, step), img)\n",
    "            G.train()\n",
    "        \n",
    "        step += 1\n",
    "    D_scheduler.step()\n",
    "    G_scheduler.step()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "save_checkpoint({'global_step': step,\n",
    "     'D':D.state_dict(),\n",
    "     'G':G.state_dict(),\n",
    "     'd_optim': D_opt.state_dict(),\n",
    "     'g_optim' : G_opt.state_dict()},\n",
    "    'ckpt/began{:06d}.pth.tar'.format(step))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "G_path = sorted(glob.glob(os.path.join('ckpt', '*.pth.tar')))[-2]\n",
    "state = torch.load(G_path)\n",
    "G.load_state_dict(state['G'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "G.eval()\n",
    "None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/yangyangii/anaconda3/lib/python3.6/site-packages/torch/nn/modules/upsampling.py:129: UserWarning: nn.Upsample is deprecated. Use nn.functional.interpolate instead.\n",
      "  warnings.warn(\"nn.{} is deprecated. Use nn.functional.interpolate instead.\".format(self.name))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fb1386d6748>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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6IqBYts0xtT9d4hqX0e9//lep6prGC0mS0TQLVH0U7E7lqo7Lv7sdgSKG4JR2\n3nlKG2vsU2shBM5unOWJR97PvT/YZW0wZtFU1C5Qtw0qSSQx6gnqu3SgHNdNBO8ZjQraoIx7JU2S\ncn1/StP609JYxJTjKzk1FqfAOj7JgE2pFax3KCVpYsl7JXmekff6ZOUYjNIc7NM2FeiA4Kf0Vtdp\n6hTE450jTzPKtCCxDb0V0Inj4GCKEkiSBG0rTFdqfrwbc7ngus1qxsRwwhY5IatpmxCzPzZgZUEu\nOWujAetrq5RZRmoD0jeoi1fkwzK71AnGJlasZtaieU5v0Kc/nTCrDvFBaBvPxtYmK+MBs6qhWlQ4\n50kCWHua8X579t4ABQUNBm3Bzxa0dQvETEFihMwIuRWqtsF3Mb/XuDfA4JB2DuKx87v4xHYVywEJ\nMdXlg8e7luAcbVsTfKwkWykstl8QBr3YExJQ2yG/Qtwk1VXXLTlsUNo2bn/2oSWIsHf/Pl/9ypdo\nXYx5+2VGP7NIalgZpvQywUoW36NuomCZZWBCFPJM0oUrxKq14BFtEW0xxpNpzWYv4aNPPcbvfuOV\nyFC6RQZEgWu5I24ZCnQ4dvP6tW6iRTDM8oy1lT7VvMJ5xQu4xkW6b7v0HMTwwFjyLEO8kucFw96I\n1CY8eeX9fP8zH+PJh8/zoccf51e+8Dn2J/t88Q+/RL9IqZxibBr3B+gCXwl1V90pIkhi2bq4RW+8\nSlYMkFz5QG9Esmw0x/DW/R4vSLsLk+76RAMEZV7V7ErN2ZlHnICbkj18nrLIGAxXyfKCNMtjP1sh\nLTPa+giTOHqSMnQPMa1SsnLKeLJAN7cpH6kwB1c5nDqmBwuCGko8iTFUrsuAGIHQVZaGzqeIor7F\nec+8asmc4qvAqFhDrNCTQBFmPPbYo6yM1xmOB4ixtG4Rt9Wr4tqa4B1tNUddS1vPCd6jeGxiGQ5G\nLAZz5P4hFuVg7z7t/IBFP2MtucDuDcvZKw9hrCFJx7HvCHy79t4ABQz4BG0c0kaKHlRjBtortss1\nJ9bEHWSuJUkUuprzEFq8B1fPoTk5Z0B9ILj2GBA0REFLJWDEx118WYoUOVjpRPYTRF3WKBzH3F35\ncNBuM5TGzMOdu7c42L9/skglgsnrdw5JtdsWvaTewSFquzTn0tPQ3TTus/YOgovUGDCpxaaWS1vr\nfPmVjKmPNQumOyPhj7NjNrDMMniP8y2Ni244sRZTCE3dIjaeFWC6tGOiMB6u0E7u08uEUb/PaLTG\n933fp9je3qC3MeB7H3uMu5PrTKspc9dwd+8et+/eo25rjLf0cosvFDePmRpjLHlecPUPX+bJD30s\nFg6ZBEml22Me7WTP6VJLkOX/sRajAAAgAElEQVQD8e8OGYJGaj4/2qN37gpbW9ucPbPNYDiiKHrY\nrOj2JtiuSjIBBDEpNrH4NKBmjvWWvCwZDoZM9g6ZuYbWJSxuzqmrGum2vfvuJl2K8aR9HMfvrm1A\nuv0L3lDmCaJKJsKgKHno4hVWRqsMBgOSxILE3ZgawGucV6oxjY21BGsRVdpuiiTGkqQJ1hhaF46f\nGxQW0xnTw5zZ/h55kdIbjCOT0j91oBAXg3hHEjz+OIMitEEoBTLxrI/7DMuM4CJjqLwjSRJS6eJd\nm8ZObON2VdUojpluU1NASNMMKylZuyCzATvowaCPhpj+xIAEPT6DhXDSlqi4Cy44nGtxvsUDX/vS\nsxwe7EeFHqWqWzZ6GasEjA+YqK2DLSErUJMQ1ERK6TsgWC5c52OoQ3KcztLurIeza33S5XkTy+3P\n0AlK3clJ3U5CWL7l8QV05cOBo6MKa9MYegXoDXuMVzPa1rE4nGI0ipcfevppRAzPfPzP8NClR3j6\nI88wGK0wWF0jKzI0eHxo+PRP/gST3V0+9rFP8PzLr/PTP/cPuLN/EFN5kuKosdbgQyx42ti5yJ/9\ndz9N0Suxx3nIWDi2BF+Qrj7h1BxZUiBOfqoqs8WUtdGAR5/4ANs7O2yeP0vPBLIkI02yuH8jSZEk\nxfS3CfUhKlO8a5HgqX0UcLOi4MLFc5TDkmL3Hreem5DNFvRMTDeWiVCmKTcmDkdAu5ucite9D0xn\nU2YexAiVh9wkrCSW0aDg4x//JNtb22xsbJOmGWledmXjnaYVwIjFWoNVCNKizhPUktg4I7K0JC/6\nKFGwbOoGLZOo2RxO6WfK3bfeJE8tKxsXMGJw33708N4BBRGPNUJuDVlmmTjFq2INJAbyXsawV1Ck\nMeYXYyiKApPEKkJV8G2LlIJbNN2ED11BVCdQGcOiagmi1C7gAM0z1Jq4CI/3pNOJjctt3HQIEUVI\n3xWVOO9xKkwnh7HasFvEpvP+n3jyAmkIBDW0dUuQOdW8xSgUSSAdjEjHgimG0EwAJdQL1BY0ZLjF\nFDc7wlULXOPIEkNqo/yhxzoCXehwUjCs3X0nDT8p0XYhtjLLM7xrcM5RNzDc6LPWG3PzhTfwfsbK\nYMDqWo9zZx7iySvv48z5c2xsrpPmJTaJJ2KZBLKkpG4crYdJ3bC+ucqZCw8xqV5mNp/hWiXNDYuZ\ni+FgmrKytk5eFLFykmUaWGI48G3Mlfij43EaD6AZ9vok/RVmTcmG80iZxacGwC63sQtMG1w75fDO\nLbwIvSKPe0NsPCCl1+uzEhx121BmKU1iKLIEL4E0EVbXt5m7OzgZnRJRl6dkSaxO9J4UWATFZDmu\nbkmLlPXVMeOVNcqiwGYZYhM8htYH6tbhmgb1HtuxpKCgQWl9oHWeJnR7WyX2Y+O7sx2Cj6dRdRVg\nQQRfVzTzI9DQ1YH8KRMahZjtSnzgfJFwdqWHGa3wtVeuM5vOcBWcXVthazygLDLm8wWH1YQ3rt/n\n1uGCuWuo5x43b/neJ5/hqac36K97wlFFW0eq/QcvvcWbtw9YOM9oWHLu/BpHIeCyIc4J2CwO8rFI\nZ9/hlJaLChHarqDFuQgu89ksCpiSkOYrnNl+koG+wUefuMyw7DE5nPEr//I1bJhy/qkz7E+O2Lv6\nGm/u7fPv/5Uf4/FHHmK8sUFQz0//w/+L/du3WBlk9IZjVlfXSNVg/RH9coVBWXJvNx4qEyt4OAYy\nWbKZ45ABTgqAIhjE4ixhfXOT0XiD119+jmY+497VBWW+x8VzO7x97Q3aqmKjLFkc7PG5X/1lQt3w\nzPf/AB945uO87+lHSbKcezdv8Qcvvsov/ItfRwvP2XMX6OUljz36IZq64drNt9nd36Ou5FgLHa9t\nsbG9waSeM04GxNqn7sCZU+HDcg6fnsp6+qFlNCHCoD/isSeeolalbSd84f+9ygefvMTqaMhqH0Iw\nvP3y6+zv7rF27oOMHnqM/hMXaO/eYLF3g8V8Qb8wZFlG6pVxb0iyk3Nx5wZ3xdCzKWIMV8YrrG0N\naWvPS7f2ORYS6Co5u3qPosgYFgkKzCZzcmN4+OELvO+DH2B3MmfvcMIr//oLTOYzJq3ggXM7q6yt\njji7MmaY5dTOc+f+IXf3jnj57esoyrBX0M8t53c2kSTFI7TOs7M2oJcnDMqM/iDHWEOaG/L+GCXF\nv7sjv4W9J0ABQLzD4FnJlCsbBbPEcWm95G1fU2Sws3OWNEsIAjd3Dzk4mHP7cMGdwxmv3LpN5eBM\nnvE7X/tNxF/h4UcusXP+AovJLd6+cZtnX7rG/sIRjFIeTeitlNiVIcd7e47PZjyldy/vOha5ovmo\nb+ED0fOGLv5VT2j2OLr/VS49dInEGkLrmM4cNoezG2usr48Yra1wbmOHRw7uMrl3i8UoY+3SB3D7\nb3GpVB556jJFPiYf9zA2I9WULA1MDu7TK0skqQhAUDlVkrIUGk+N//EvJzH5Ml1Z5j3OjwfcKfss\nmgV5KpzdGHHxyiVWNtdYKQyrq1sc7N5nZWONld6IrG052L0N9imCd9zf3eWlF7/OyrZQ14ZCHEPj\neejxR2ir+8ybGfePw6oY6uRFwbg37Ap+QpeJjdvICe9mCqeVhdN5CD11aYGiKHCu5etf/RL9rXNY\na3nhtZs8fHaVjQ9+AF3MyAd9Vkzg2ttfon799zlz8VGGww1W1rfJyiOayR6uiWGOMZYszVgZjanm\nc4okYWtlwMPra5RFwpPnPK/fO4qtOi3sd2FQnhfkAoVAmxoya7n4yBWy/oC3965SeKgEagxNPceo\n5/6tmuu3r7O7dZYPPHSR2XzOS2++wa3dQyZ1Q5om2ERIguCcR7rt8klQenlCLzMUWazY7Y2HbD30\nBIONi9i0hyXgfP1tr8X3BiioIiHg2pakLNgsSy5d3OLy9jqf/9pVFosFa8MRZZaTpAnnLpyl4ja7\nb7/C9tbDbGyPEQyf2BxRaks76JP0UlQaJuq4X1c89eSjvPDWTR49s8aiqnj4/Drlxirmi9eR0RBp\nG4xdHhC6PE5L4vFey1N0AIyyWMwBpfGCd0KWFfi27jRDpZrP+dhjlxnkBVlviG0E27zAa3/4Jvea\nD6Ozt/nEJ/4SadmyffkRPA2YKSbN+NS/99c4uHqTf/Xrv8HzV9/mI09/lMNvfJ1zZ0s+9kN/hk8+\n/T5eu/07WA3xwJXljrhlyLM8pUlPSpgN0pEKiSIWcHR0xLlnnuav/81/xM/+9/8DR37OI488xPnH\nn2S2mHB/7w84mt5j9fx5WrPJU088ySC3VM6zf+sm47UB06bir/71/4Tf/dxv8sVnf4eHLj7FnTdf\nJF2r+OT3fA/FcMDrb7wJy92TwObmDo889jiZNeDjcWZRWItMYVkBubQTQFiendmdONXVU4gYDoPw\n6KWHKYv3QWqZHu7zyJVLpH6O+oYky1jbOcNsv8e0SWhMwpfefIuV4hpXBiPOXjzH6tp5vKtoZgeg\nggRhfWUF51qevnKBMWBcS6qOD1yyXD1KkaXqs9xEp4qxCaOVVd7/yA637x5yffeI1Hgee+JxcjvE\nGYvOK6atMnSO3as3+cnHLvDrN++ydeUyVx59mO3BOl95/mtMFzUrozGJbwltw3q/pG1byl4fkxXk\naQIG8jxhMC7Z2lzlmU99hNWt86yfvYJJMqrgyG3RnVXx7dl7AxQQNHhCUEI5Yi6WURDOrq2yPb7P\nvD8ikSQeCiLCxa01ttdWuHJum+mi5nB/wsI1hH7J5cEak9EYUkO/7LHSKzm/s8n+oWft8g6LesbF\nM6tcPrvDnguoJGBShLabuHE3niyZA8ehO53DY5BA5ZRFULwKO9tnmdxTVnPYX1QYElbG6xgRsjSl\nvzLmR378B7nzxu+QJFsUGw9x7vI5knAJKwVte4hogU1bkvu3WFtb4Yd/8Af42GzC67/9Ik9cucBj\nP/QJslw4v3mI2IzaBZJOuIvnJZhuP8Apz9pttbZiiOkOaF1kPdOj+9y6c8DZUcJ/+l/8Hf7n/+nv\n0svGPHruIkWZU8nTZHWNlZz++g5F3iPPMw7374M2iM3I8pLZ7V0uXrzMuN8jV2X98cc4e/Es9XzO\n7qzGWhuTYRq3vSd5jocOCPSYJQTt9J9TlQqnOULs/+6srK5GQIwQ1PO9ly/y0OYq9eIGg8EZyp0r\nFP0+fuojlQOKcoiVhPlsSqvwFz7yNEWRMuiVUfDL1nGTXZrpHtrGMCZPc1aGI8aDEbkGcC3Mjrg3\nq3nl2isoP3gyhU8SPKRJwtNPPcyVnTmf/8orZDbQz0sGvSGDcR+DsrGxzu7uHm9guBqEJ556ku3L\nj7K+NiYNKf3hmCuXr8SqysWceV1R9HsMy5yVtXXqpqaXWdR5VoclF85uc/GRK5y9/ARFf4U074FJ\nqJsj0uKP+VqAb2LvEVDQ44V30AYOKs9ZsYhYrlzY4e60BgTXBDIbyBCKPGN48QxtgIP5HB8UPGQ2\nJfE1wQi+8RRpwagsGRXgXE7r+/RHQxBLJQGXZASbIVTd2X7L046PY4eo/h8DBYwGlmQemFQOgnB+\ne5udzSH/1U99mpdfeJO/9U9+gfm06sq0ocgsw9E262d+Et86TDkgSVOMyfCLBWJzVON3JWS9gqS3\nRp6nbMxLHv4Pz1H0C6xNWBzsMSwLbBZPjYpHn8QzITEnZxoutdElq7XWHh9C60MUsNq24c6dm+wf\nHDIcbyMk7FctuU3YHK0wGA7IMwsmIRuuEFDu393Fufi9FL5pMARGZcaV73mC6f4mh3uTriDU4Nqa\nPI/fU2Gk2yhlTJcS7U4skqVYKCe6wrGdFkpPCWUiXQWj795b2eqnrGaB3vojiBhskjGfH5El0hWh\nBmxmSZMeq+sj1ArlYCUWZBkbK/+Cie+pDvUtmIQkSSmLgjwryY3SNi2L+YLXb+/hOgd1fHRcJ3So\ngk1TLjxyhemk5eJbN3EQReJE6ZcDcA5d3yC3GUm3CWr77A7FeI1xnuA0ZTTsc/bMJuPhOovZPapF\nTW/QI0sSQrCoScgtmMTykY89xfmHH2Nl+xyDtS1skkUxWIXM5nHevvuUsT/G3jOggPGQGq41ht3b\n+2yd2aJXGjZWx0zbfeazGUeTDN9k9LKMLCuxeZ8Ey3qxBlZIMkuzqGHqwBM7rten31bkKSTpGDEJ\nPgTu1XPePKhpkyFa1Ui3XVck5pTjpF2eYdZtNvLxwI5bu3Ma5xAsEgymGGJCIFfHx37oR/iLL73O\njb07uOYJtHKQNSSjVaRYRTK6E6MNoWlpju5jzDp+NovqcTXDVxPKtfP01rcQk8EigHdU7j59dayN\n12iTEu12QibWohiCAUIsVBFAu9p3r/FcBRHBZAIqNE3D8y9/nVe+/CK94XWSQZ9785rDacPq2NDW\nOf1Bn6zMMXkCeKrphKaqcG3M1pdimB/uEy6cZf2RR9lOE4y2TPZrXHiV8Opr0DEBMfHEqGp2xPTo\ngFYvYVSwdGczHIcNJ5C81EmWoVx3V/xhLMYmmER5+OxZyiRhmKWkeYa1GQd3b9Hb2KapWiQxJN5j\nxLKxcwFtFqhrsVkf6fdQn+L2ZtBUoAHfLqiaeLjqymhMtrIOoeHuq6/x5iuv8tXX3yZIwokCHfvU\niEG78dh59EnmhxO2njignyQsFp7UemwawEPZWycvV9neOIcK1IuKMu/TX1tlWjUMRmuUhweoLtje\nXieRHsH7eHjrbM58cp9PfP9HefLJh7ny2EPYPMdkBUkeHUj84oEQ08tuHh3Jt2nvjW+dXqYfLDgR\n2izjK2/f5ebRjCDx2Oy70xmL2jFf1Mync5q6om0b4v4vjw0edZ7F7AAjXazqW6wE0qygv5ojWYoX\njzOeo8ZxZ9KCTaBtYjN0WZQiJ8Jj8LH4KPhu6zTUritvVtDgqUzCXtVg0wHV4Q2ePH+OV6/dZ1Y3\nNPUCX82gXYHqBIM1KG4+pa4d9fxOBAoVQuVo9g7i5iQN3Wc4fFOxqBeE4OgXBVkSmZRiOk8b1c9A\nFOyEWL4QVGMK1Yfo4f1S7Y9nLXz1pZeZVIeMixFvvT2jcnN8k2H6O7HQikBqIctS6rolBCUvMsar\nq6Q2ITWGwbhPag2JFYIItXfMqmn0uKd4tarSNAuquj5O6/oQyK3EQ1p0ecDs6RRap5McE7cI1qoN\n1iQEFarGEUIcO++UunGkWUZqE8Jkhq8c9WxBs6gQY7B5QdIbYooeIn1ACW6CdzWubvBNG7/RSyPr\n8XiODg949cYtvnZ3QprlBHXHFaQnAnXXVCsInqJX8sjFbbLekKppadqatq5xradta1xb41zNbHKA\nl4ZsfZ3auFjgVQyoq5rpoqYJULsFrnXxy4e8x6QpH/rwM5y7eBFci84miDYYiVv7hYARSGxXq/Ed\nLMf3CFMAMYqkIHlK61OuLir2n3+dxzZXacVis5Q6BEwTePX2XTZHI9b6NVmWUwyGeBFu3bhGz6Ts\nzStILXl6FtQxr2r8tUPKMmfuhMbBV/9/6t40xpIsPc97zhLb3XKvqqy1q7qqF05PN3tW7qRIy5IJ\n0rIM0wYIGfpBggYkAzYMLyJs+Id+GIJtCLZhEyYlGbAFm6Jsi7Ip07RmhmwuQw6nZ+tlequla6/M\nrMy8ebdYz+IfJ+JmVg810zJpohmNrsq8dW/ciBPnfOdb3u9978/5xsQikghhGkTSVnNdcGcFoK1h\nRXhkbTmKFIUzCCKkd9SmBa9YuHvvXbYUTIqSRKc8e/lpfvDFXXaPjtAtNDpOv4mIe7h5RFHWzPOG\n+9f3uXv9VWaLgu969hmkb3j7xtf57Hd/mot5jBUlup+hequYuqR8fMAprXh56wxvTgsKH8Ayuwcz\nhGoQtUcqz2i0gRY+eAqtS+vbmvcSA9Rm8v/J73yOe/mEpy49zTOxZlFWHB7eQNh9eufOIfFBrs55\nmrLh6GjKb33jOs/sHqGd4V4z5rL9FJEyOGNpXMPuZJeHDx9y98Hd0F3YsjJ1iljvvfX7PP/Jz+CA\nWIVwpjOyHfquw1mIE8YhrMHwu5RpcNV1ym+/8S7PnB6SxIJoMGA6OSQerpKM+lglWMxylClBCMrJ\nAVGU0ButQznFmDHWNFTzMaauaIzHeIGXgnlVksQJt+/d5r13b/C1t24xrQXroxGlpWVa8kGnxNEa\naY/0Dq1DzuOZF58lnxtu3nmArBQ7taXnLIkL9/lo5xGlsVzYvsjk61+jns/Rg4x7D+9RTY7Iq5p6\nPGbU76EiTZ6X1Drl7Pkttp86T5oq0l5Kx6WJDIoUUnkkBiVACUXl/8wlGgEc3lusdwgFjfXM64aj\nRYH1EEcKITdCr4BzzBYT+lGEtYFVyXjLxuYG6zrl+utvk5cFh+MJ8/Eudx484Ed/5IeYNzVOemaV\nZWdRUYoMYU2brCPEq8vLcXgFTkkapWmkXHYkGhN2cec8VW05e+YC+3fe5pvvvs+VF58nWnU4UTOt\nCoYLicDQG/SQdc1sOmYyz1k9c5ELl/rU+VmGecHBwWNUJJibEt3rkaxGTB5PqeoCXVbUdUFVVShj\neUkbShXzet3gpcQKS0IEsUGpCHxDqlMKVwacwgeATND+JQWL+Zwv/c4r3Nh8k4+/9An+4N4BaRyR\npDF5XeOjGNk0xF6ztX2atZURItbEkSaNNNvpRWb7B9TDAVpLjABTNORFxe0HD0Lrdvt93jl0knL2\n0ieo6posSYJylLNLh2Iph3eiyahrNDoGZVm8a3A+Q3rPe/fuc/PhU2wONMoaKuN5fO8BVA3O1Mzy\nktHKKtlwgBeQFzkyzkJZtK161Y3BttUQ6wPbUmE9N9+7QT495MHeIbkNkVzUS4hn8XEGuu078S21\nHNIjnEFJRZxlSBVjWli8aCrquqYxhsYYFmXOaG0L4z1HkwnTw10evr3D4/EBmYjIej0KqbCzGZES\nzOY58dYqq+tniRKBSgQqiZBC4EzV2nqHVCOEr5C+CTmnfw5I40fCKIiWxsoLj09irDehy6sXM6sr\nUh2hvKYxNZlK2IgjaqCyDUZAQoyIMkzjOaobPvnsNcq6YTGZkq6u8/LTL3B3ekiFpfaGnaJh349A\nB3IQoSVCdzDmMHjOCxoDhx68Dqw4QmmEU5R1jiVQaQlv2bv1Fkfjff69v/fL6EjzyY89zSyf8sKl\nizjlsdIzqxYMNzfJ9w+YFzn7r72OmTym8TEXr3wXvX4fBTz37PMIW/PmjZsk/RhbzOlXU6zz7BrL\n23s5r+zu03iFTtfRcYyOoyUTkbENWS80ZOWLJb6Zb5kSIngO1hicEBzs7fH+W2/xV19+kX7s+VqR\ncz4SXLqyTt0k3L93n6GCza0eWTpE9xJ2H08oy4bZ3iHF4wMK4znMFzy6f593b73LV776KlXdhC9r\niXG3Ll3lhU+8RNbvYZ0NfAg6wriAeFz2iCznhnwinAiVBw3oQOziBHf39/n7X/g9nrvwE1STGYeL\nkrVen92dR6xubbF29jxZ3AMPFoNXnoO9R22oEiDk1tSAoUFjVUruLHUxZdSLufXGIaWziNGIQaJ5\nOC5AJXgpW94IcRxBiDYMbfkUpYAojriwvc6sMCi7oIkEi8mURVGxtrZO1ssAz+poCE3F3sNHjESP\n4aiP0JK8mFE5izEOi+DKqcs8dfUySldIqfC1w4vAs2RdHbqIq2ZpqIytn8DZfKfjI2EUwhGAH8Ja\nIuc4s5qyKtvfrWRzrcdsPiWRq/SjmERJnJSkvRTrPQf7++xev8/KYA2tBQMtSSU0tmFaW+RGgheK\n3DnGXlJGgdBEeI8I4hLhMtqH2s3MFsUKotOB9DTVPPA5moZiNmM+GdPUFQpHZR2f//JbbK72cFIF\nta4oxkmJaBak/XXMzj4H+wdsbZ3jypkzuKhHtrZJJDWTR3e4eesu12/d4Mrlba5ce4r+So98XjI5\nmPDa7RuUYoXSOkS+oC80DQ3CenQUMPO9JOJgsaBq/miXsYP/LEt9LVT24HCXsq7QQnG+lzEt5hzO\nKoR0vPvebZqdA1RVsRInSOdxrsFIR//yU2xeOo9KNHZhsNZz4/3bLObz5bP1LvTpJdkIHbXkLVKh\nWglPZ0/G5qJzG05c8ROmokVnOrwziEjxaDLFCMnqaAXjQFnIhgPS/krAaViLNRZr89A70+ohyC5f\nIR3OepwX1BaqssTbhqKqcL2E8f6Ci6eGVLUlicE+Uf49cXni+ApDA51ByIjBsE+eH2K1RtSGwWhE\npAv2d+9RLxoSlSGQmKrCzxtsnnPv8T4kiqQX0++lqCiU5ZWEKBtg3WFgKyNwRAatTAtOhn6fVsYu\n0AL+KXkKQojbwIzQOGa8958SQqwDvwI8BdwG/nXv/fjbnsiDUBKpNH0liaVk//GcA+e5uJJS1nPu\n13Pi0Yh3dvd44blr9JIYlY8R5Jw+e5XhRp9TvZTFfo2ferw3HDVFiMVtSXKmT2UNb+wXFMkQn0U4\n4xBKhQXflfI63YHWbQ1ebAunaZmHrvQj3j/YRzvLwwe30N6iEbTMiHhTU8wK9veP8HWNiBLuTWsu\nG8vTH3uRc88/RXWUs3g0pno8IxkOiLI+1hh6q0Oef/4lPvuXfhyhA8HszltvsLBw/7BikSsqM6P2\ngr3DAyIe8/Hnr7JxdgsWe+STkoOJoXGeRzfu0uUOlh2HS9BFWHQdGYr1gqJu+PJXvsT40tN810uf\n4OHOAbVxvPTyx/meH/wE777yDfZffRO5Z8m2LhB/7zbxKGF05SxOSRZW4UzDr33hC/zub32Oqqra\n72gfMjBaWw3CwKJteBIyCLAIEWDCJxa/b3vWn3iNTmU73FfUP42yY6yAf/DKq/zED36GXtLn8bvv\nUx+VqCpCRhFSCbyrSIYJ8aBPOkywdYmpy8C8FY+wiynTxYR5kVNZQ5ZlPBpPmTRQO8e4KqgaQUaE\n7HW6kRxXIDhGleIDBFoJD96SpRFZpCjmc1zTYBqHUp7z586QqQQKQTErmR9NWfgDhnGfKE1Ihj2s\nNFhnMHiM9Jie4s7tb3Kqb8EcMexv0DQ1KM3GufMoHbdeisURqmnOnoCQf4fjT8JT+HPe+/0Tv/8N\n4Ave+78lhPgb7e//0Xc6ifQQCY9/tEsxrxhtrZIqeHBwwKXVEc+f22BFau7lFTt7O6yvb7IxSKkr\nR1OU9NYdYrWHkYodu09dVhhhkZlA6ozSO2YWFjKiRJHXNVbK1kPoWI+6mPtE00O7r/p29xJ4PvXU\ngDv37vLcU+vcvN4gcQwjQZxGkPQ5Gs9QCBZNTbVb0BuMOL3WZ3q4oDjKSXoZKu4xOJvQX9tC6hgv\nJEbV6FNn8NYj1RBnp1TTCbPFApOus24GfHJ1m68WC4xOGPQbZo3FuAJhPFEvw3vJ/uNZAC356FvG\nubtD8cTGK1pwkUA0PV759V8nLS3P/8D3cXRwwKyqWe15zn36aTZeOIOY1wGY1euhkxinoDGG8fiI\ne3v7/NZv/jplVdJJ03XZzY5gpWlqtAhaC8cNaJ2T0OYTnuBn9E9cP0tuAE+92CPRYIXma9fvsDZc\n4RPPXma4tkKmFZnyyFanRSUZ8bCPiBTemUAAYyuEEDRVTmkd88UUL2DYT2nqhrIoKIqaRPfxTmHq\nhkWj8dq11+qOxxA41kFtDZ3zOG+YL2ZUtoamAe8wdU2sI6QKorvjfMLB4ZjDgyMOi0nwqvoxiQ73\nbK3DYWjKkv1vvk1vcQqTherbuStDvIiI4x6zvCCJBUkUdCmVCuNo/5SNwgePvwT8SPvz/wi8wncy\nCt7jG4coKnqDIWunNlgfOYbCk65c4PrtR7gzq6xuDIj1Kd5+/yE79x+xdeYsq6M+Uiq2/AZKpgxH\nPVa2rqKyjMXkiKqpmdc1rz085PbumEk6oDFN20cfwC+qm7yCsDO1ABzfoud8K08eMuOS/+nzX0TF\nGV/40utM5zPGZYMxlsRFDIQNxBa+4PTGOr/7h9/gvQd7/Pnv/QS9yPPF3/0iT11+mqvPPheqkHGF\nl4CO0D4Bm+KUJp/tcJhaIwYAACAASURBVPTgJovJETfvjfnKzde5eVixng3p9UY03uGihCyOuLt/\nD2cLLp4d0BMxm0qzO53Rax+voMvbnbAErWU46f5qrfiP/9O/zptfeY3X33yH3/jlv8Mnf/An+Ir7\nA06dOsPT58/RG/ZJNkPm3+Bompp8Omb/aM7P/2d/k/feeJOyhYF3ScIlESuQ9fqkSStttpSUbwOa\n1nMQElrQBUJ4Yq2DBF9nRNrnEvoUAjORdXBQOX71i3/Izce7/NyPfR9KR0z8IT3dZ/X0udBR22pf\nNE1rFBwY63j/+lvUVcWpU5v0+320VLx+6w5v3b7LztyRqQG+WlA5z8zmlIdBg0P4LhHaMVa5QANo\nGkwx5t5exJtv32ZnXvLyUxcZZBFFXpE3BXldMlg5R5zFnNlY4dTVM0hiFsUBRV5zf2ePRZ4z2TsE\nYZCJpcwNj/cPSHsxYm1A1OtT3D2AOMHKKUQxMoo5vdYjTWNOrQwRwqHlt24Q/6zjj2sUPPBPRQCl\n/6L3/peA0977RwDe+0dCiFPf6STCe1rNI+Qgo3ANfuqQ/R6RrOjHMVUTRExWV/qcP7XB/lizv7vL\n+EBRl4bptODKM09TlxWp0jSzGVVRUDSGRWN4tHPI3HiMdhgtWvBLAHo5AtYdggt70h2kMwbLKDy0\nL88XOUfTObNFyWRRhvuQikpqiirnzEhz8cwWn/3Ys/zhN2/w7u09LmyvIpucxbtvc+70JsJBtjpE\npQOQ/VDSq+d4WzF//Ij5ZMq8MNx6tMtbDx6jB2uoNEL7hj6GWVngZcRqbx2cYDyxbKSGU5ubuAby\na2vwercFf9D76Wr+7TMQAhwc3H3Adz3zXVy8eJlf+IWb/J//8O/yL/zkT+NmC9bjiI3zF0IbuHc4\nAWVe8O71W3z1zTe48eYbVFV5YnKEStEyHAPm+Zy6qTA2W9b5jW1r6y1ZKSi8tLSSmsRKBbEVQQjS\n2uSjlBKlFKHPUOJwLBrL3fs7LXAtI00T0jRGtXyK3nqcs5iywDYNRVExn06ZzResjAasrqwQRQqM\nY3d/n/nRmNxkSFfiJhXGOmqvqY0JI9jOl1BObXshpMIiqdjil3/t/2b/YMrLLz5LJBKEDVWJSAV8\nxeHBIYPhEBXHoUrgS47mFfP5nEm5oLIV8yonThSm8jTWUxc5kzLnVHoKn/RoPGDBOB/Ipbxnnpfk\njaUnBUoreslJ1/DbH39co/D93vuH7cL/nBDinQ/7QSHEzwE/B6DSGG0bYumI6xpdFbz83AqZl8wP\n4My10wwSjfdQVw2Xttc5tzFg/3BEVTUsJo852rvPu+/cIOsPcVIhpSAv5hRVTWk9O/01mt6AxDUo\n52mEDh6B0ix5Dq0AoUIC64k4VrTgNQlCce65q9z+xg28c0SEQbfW0RjHeDqmHyt+9LlrjHzNy9cu\nsH1qg1/54lf42q33uLq1xmJWsp6lrI6GnN3YJBttIJM+1lkm40fkVclrr3+dd269z87hlK/u5sT9\nAT+8NiBSjvNRSixLZkJQNaC0wpQLjNDc2B+zkTacGvSoZH1yxJd/L72e7uc2v1DWDf/5f/O32dwa\n8vy10/w7//7P8+r1d/hHv/hfYuY13331k2xfe5710RkW0zkP8zG37rzOJ3/o+9kd71GWZXuuk9/H\n0rX2SL7+269w7cqzPP/MAC0EjXGUdYNUgsgbNlY2yPoZfjYhpQahsX3B/mFQ5aqsCUKqQiyp42Qr\nyqOsIU5iHk8mvHn7Di8/e5mV0ZA4TmiKBjFaJ398H2Mbdvbuk9c1k8mczeGA7/7Y86S9PkoFiv3D\nxQQtMs6du8TNd+4yrnNm5aJVNddEOg5pg+7eWpi6cxLTCN5674Bf+/yr3Lw/QeJ5590HPHfxGWw9\nxpiWTBhBnt9i517JZGGojWQ2mWG8wBpDbQ3gyJKYIHviSOKI/SPJ+KhiPCsx85I0iYmUZ2NtkwTA\nGhITESVDvItwVUnezD70ov5jGQXv/cP27z0hxK8CnwF2hRDbrZewDez9Mz77S8AvASSjgdfOEgMD\nJYi1JK4Fw57ExYbGGoo8Zm00xBpDg8MpGA5ishjSqIcnZvfAMzma4wnJK2MKsp5idXOVReOZlgvM\nYBQytNaCa6m5hAodkkIE/n4nAisSHRFYJ4Ue3ERdKPrDFUx9l6oOkl5CSmzrRZxdG/LM9ibSeVQc\ncXZrgyvbm9x4BIt5TlMWfO2Ntzh3ZpPF0QFp2iNJMhyCyXzC3v4ev/Olr7BzNKdxsLl2kUx6qsow\nzBRJKmhUhJ+XSK+IvSdJ+0EHMY45qkpyZ8gy8cTaBI4XrBDLCd0dzjt+6Ef+MhcurLC5ucrpzU3+\nwsZn6cv/gFtv3+bVz/1jbj14F2kdHsNT3/Mj/Oi/9JNcvHiGN95+K3QJyq6fgWWSsTMJQsDeg5v8\nb3//l/hXfurf5MzpLVaGKTqSOOPI6wYtFcXsiJ7wfHrQ40bhGCuPkA4VD4lMA7iWzPT4vErVCONQ\nRHip+b2vvM7mcMgwiohzjVcRZrrHZDLl8PE+9x/uUinLj37vZ8hUTL/fJ4pjTF1QzCdMpxP6iWd1\nlCEVWCx5XYVUsrAUZdVSVsjWECqMEZga9vanfO63d3j//d2Aw9CaqrKM5yU9E7wsrwVeVphcE2en\nkPUhdbVAxwnFIg+s4EKgdUQSR4EsNsDlaOqag6OS7dxgpafOLEMRY40nXV1FuholI6jmzH0Yp17y\nhPTZtz2E/+coVTzxwYAPld77Wfvz54C/CfwYcHAi0bjuvf8Pv925eqO+f/aHPs6KaPh4ZLi2mXFl\nY0gUJTQiZPUXC8N0fETjPcP+CCVD2UVZj2sMTd1Q1RVGRPSHfWIdMckXzMuS8WzB7fEY4oh5tkKt\nFYusRxWnTOKMSkTkLsJaibcylOhaTv8Q8wbPQfsEKSIuvvb7bA/7vH/zPkfzCdOWsFUrSaIVP/8X\nvp8LowHXrp0mHgyIswHzRcXdO/f5zT/4Bht9TVPMMc7SypvgHVTWsj/PaZzjY1cvsbq6wsJJvvD1\nO5xeW2F9dZNhbDk9ipk3lr1SYIwgF4qqrnl//oDFUQW1QCeanIabb11fjvOSrWmZFDvGBIi2Dfvy\nqTP89Z/9WT7xwlU2L14GFLa2IDzGhwktW81EawxVVfP5V36H/+7v/PccHhwwWo+ZVjU297BcMMdo\nRK0jlNLEUYKOE9IkIooTtE5QacIFJNmgYr4osI2lMp4qtkwPDbM6UMuFkCHkiGVrGPCB7Fb7IAmf\nlyV4waA/YCXVnBsM+di1S3z/p55j2M/oDwZIKUlF2PlDhOjJZ1MeHzxmWldcv/OQvVnO//D5r1Ib\nSVnXOGdZGWyzKB/z9pd+ATBIYmrj+LX/58t88cvvcrSo6Z1+gfmjdzHGkp3eoHr0gO2tdT7z/FMk\nWPq9hMoGmHOVG6bTCZ6aqrYczAINvfcarSXOHBJFWyA8VkjeuH6XxgsuPnWRZ154gcP9mzx7ZpvN\nlSHYA9a3LmK9I1GW0XCIdB6ZOj79F/+Tr3rvP/Wd1vYfx1M4Dfxqu9to4H/x3v+GEOJV4B8KIX4G\nuAv81Hc6kRSwrjx953lqbcj2ahYmnlBoKSGKEcKSJhmmamhMQ6xUKPkokFFEoyOirI9xnjiK2nhZ\nY7xERBGRrIjSIf01yaKwaG8w0iKFZ+oCJZlzCmej0E/QdpV54U9Qnynwkmc21qgdpFoTS4lu5dgy\nKXlpY8Snn7vAQHqyKEIrSaQE6ysj+k9foB4/Zv/oCJ9J8rqkrEvywlC3xJTD4YizGyM+9cIzGOu4\nvXuEyWeYROMHGxhl0VayHUfsz3KEFRwuJkxnR9TOIC1ceOYZ+jIiVXDzrRvL+snyaDv7ArOyxJvA\nEekd7E/HfO53X2Fto8fGuYsIpQJc2rdQXjzGBIHfxkBjBbduPyBfLEAIqsodV3E6PocoAhcqN6KV\nSFdphO5llPOcqijQSlM5wfbFC0yZEA0SXJlhS4vzFdEAxDiI8Pg2v6BVMOASSxDxcTigdh3rsmX3\naEy6ucpf+ckfYG11yLAXoXSQcAv9YqHl3LnAaF06Q4Gndp4siYnLqqWbCy6PlIKyyfHeIYUORMJO\ns7uzxyt/8DZFYSAeUtkqJFJFifcG7xoOxxPGkymDuKHf20Z4hXOKKJHoOKKxM4gMMl2AkWi5Bb6i\nVgKhgsCtVprGSeaVYWw1fm2N3W/MuLx5AYFCeY+0JqhiJRE9FQxLof8UuiS997eAl/6I1w8I3sKH\nPmIlOBsLMq8YxDGxiog7DjsfFJw0oU1UCwmFJ45jghCHDwK0UYw2XU8CQGCBTiLJvCixtqQnHYKa\nXuTxVuMaiXMKvKZoAjFJRWB38hy7W0IEIyF9hHCwFmvmjWMlTTBVxrjJ0UrwmXOn+fGXrrCaRiRZ\nTBylREmGjGPwAtnPePbqFdYfPeD+/a8jsjmTxYx5qYiiVZSO+eyLVzm1tsKp9SH5vCITjqKsEXUN\npqHyFYdaEsURh0cTGhXzaDrn3Ooq2/0VEq1JnAxNOfG3NsIct/v6gOY80VLrCc027966xevv3OTj\nL343SRYho9ZAWhVKbDZUbGoXGrbev3sPZ0OXqTE+CNO0JcgOGh71NFJJhJXoRJOtb2BrjyCU1RAa\nfMPdRw+RCgQlQhpUJGiEY3I0R6UprgoyeJ0upgsmPJRUw10hhQhlxDhCS3hqa8j6ao9IuKDc3IKp\nhIpaJW6wNiAqq7qhKubM8wqkoGnvA5m0HJINxszaMp8ikyl37uzzf3z+y+R1hFcx1joW995GCMnq\npe1Q6UBTezia59hMsC1D0tq2PRBKpHg2cM4wEEMa2aBlghcJkkHLI6kQQlLWjkVeU00c84OKLO1z\n7/GY2DdcPXuayCtiqUhVRKZ121j3Z4zNORGej61q+jpia5TSzxL6wx5KaarahZKV91SVw2AZDgYo\npahM6LYTTqDjCK0saewp66AW7ISltjm18/Q2wZkJq3YN6wXrUYVzhmFTkzuJrDUzJxn7mAYFKsah\n8UikUHgVhbqvM/zwekzlYaI22J/1uJNXXN0a8Re//0XWVgcMV9cQKkiHy5Y6nihGpAnZs89w+vIF\nLu6cx1YVvdUNVJQgUxVw+K7G4CnnQYBk68wKjwvD7oMJz1b32OyNOLXdYzEvmB8ecPHsJc6c2mAU\np0TOohKQUY/Ya3p+0pZR29q5CNujIChA4cE3FqE1vqmRWuFjxe7BAf/gf//HfN/3fB9XLkckaS8U\nYYTD2oZICJz0zOucVCfcvn0zPEgpaFXq2vxLOIQ3JEmEl5JmanEqYra7jxQSHcVIFXogkJqy8ZhW\nWNjaOV44qiIHD246bYsordnvSp2CtmoR2I2TSBJFoRqRKsHRbEFlgipWnMQIJ3BHOe6URSeBlbqx\nNdYZnHQM19ZJhg1v333EK9+4gfEC5xucC/kn5w0Cz1t3Kq6/+Rr/9Ldfw6CoTINpaqyHYj7BNUeo\nxrBy7hoLf4irPIdzy8H4iO2NszgJTePQQqJk4EiItUS27f3GC4z3LUmOpCEQD5cixfiaO/dusfur\nY372r/0bHO4XfOF3fwP7PS9y6VzK6qkhla+YlyXCLlC9wYdejx8Jo6CF4PQwJlEQqSD1riKNFBoV\nhXKlEALnLJ6g4aikCvVgWeOaqH2PDL35UgMB06+SBN0YyGPSKCVT0AhFoiXGihCziQilYGwFsrYU\ntiF3DoMGkRD1exTzEtEohHc8u9lDSsXEeO40lu+9do6NzRHrw4wky9BJjNCa4Cm3OQMlQ4YciUx6\nrJw6F8RX4gShBM5WOFOjiIkETHf3AMXG5haXVxIOS9BesOE08aJh7ktGDQw9DGtLzxeILKWpHfcW\nM0YOVrRYGgQQIRRYglja9J914A0IiTeeprRorcnrin/0T/4v/upP/zTnz/XbxKQD5WnqigZLGmmU\n9CwWkwCjlRJnDR2hLICMNDpL2tKdwnuDKSuitBcWswo795Iev5yHvIWAqgkxvGtbqgNTUzemLD0e\ngMaDEB6JpvSwMdI4J1iUFXeOZhwVNWdWR3ivAnp2I3RmYmqcc9QmfE9lPGhFFCnuPtzn7v4EGfeA\nCF8VYW41CzyeL3zpDd59511y62lcja0rGutQSqGiIbU8wihPefCYOJJLRua6bNjPCyIdEYkgemQB\nITVCtaFcWxATSrV9N0Gxy3poytAk6DzMiwP2H+wRr8T0ttb4za+8y/rNXf7lf/GzXDg9wLsKHemg\nIfFh1+MfYy3/iR1Kevq9wLMiInDSBX4AaciiJDS+CAmZRiVx2xevSbMRzg2QOljTpjFURTsIIiGz\njp4YMIiHaJkitcUUgUuxbILijhUWZMUGkqEQZMZRSMlUOBZocgzNvEYah6pyfF3Tu7aBTnpYK7jS\ny0jWVkmSiGQwIBn0kLFCaBXc2abGVhVkCiVjpPIIrdHxalhoOgIcroImr4kHoV9i0B/iI0VVzPnJ\nFy/y669e5/Bgh0odMM1HjHRKNc+xRY7ob7BvDxkfzkKjloNcCG7b4DJ2rcveBar7rhTZJf86JKEU\nLc26d1jr+Pwrn+f1t97kf/67v0iSxHgDkYwQUZBnE8rytTfewtQBFYi1ITsvlz4CWmkwjmYeDEp/\ne4tqf9ziEhTeVAghUV4gcSyakpB4DR2zcOwRdHkRd1zSWFZQlATVtWh7R40K4j864ah0/Nv/7f/K\noJfymecucHZ1wOXTG/TTlFoKcusZJILHRznnVkfMbMPb79ziV37zG1TGBsFgEdCvMsvwPnBfvPXG\nDY6KGk1Etn6e+e4NkkjghUAmnqg+x6NHb3J2+yVeurqOTkfcvDdHiBVmVcR495Azqz1iCdKH5+Os\nxfsg7OJ1gpMRkY6pnAtJ87ohLyu8VCRRhBeSV996jWevrjM8vc5XvnYd+eiAL994n7Pb6/yVH/9e\nzm+sMBQffql/JIyCRxKbCBkFvkFhGqRo1Y+cb+PNgBnTQkCskQj6gyHVbIZVIUkmHchIIGXYBXpx\n4AM0xvLyx59jPt/jxtv3sK5tVBES1er0pULR+ICZOH/1NK988xHeOqS1DEYZk6agaWoEHuliRkmP\nJi1Bx8SDhDhLEFrjhMC1hKIBeRMDElRwZ/Etvr/NmksfWsadcMhBEnZGa4mHI+qqAZ/wqU+/xJt3\nDri+c0QkPNI2FN6wNuhxWW2wSEtE7pAmNEb9uc11Cud4rSyXlYZj5OLJHeNYD7HTBghj7vDCgVc8\n3jvk9a+9xic/8wniOAjISOEQOA7LBa++8SYqisLtuAAXX7Y/t/lG0SogC8DOFkjaJB/HUGalBd10\n9Pgn5A+X1/6BQtmJzuWTV48Q0JRBR8T5YOySOKMy8LXbh7yXzfm963v0Ik125jz3b98kFprdyR7/\n6kvPcH+Sc3d3HyskUb+Pnc/wrRfqisDiLKUAm5MQkM7V4jFtRIZUCmsNUiYk0TrSaw52DOmKwVnL\nvFhwMD4CESjapYRYgPABc0GLqHVCoKXEEGDOi6pmf/8AZKi+uCZ89v57OySrZ9m+son3gqasEVbz\n4NGE19+5h3zacfbU1odej/+fS5J/ksfK5hn/A3/5ZwI+eylw2pbOQk8MLHc6lnjzjhC0IwBdusq+\nm2ydm8ky0bLcdZb33c2o1p1uy2ddb3+XsMQ5qsbibEV5+5t4IXDOB/itaXDeU5umZTeqkUKRFzOa\npqZpGoxpW4OlIhn08LbGVE1I0LXXKqXEWdcmyzrUnqI/GKI64ZSTx3IH5XgBCVp1rLC4/qtf+K+R\nyOW9dH92vz/ZBNFO6nYMXCthJjrPokV2GuuOz3W8rk+co/1Me0EB0tzmMLovaROenURaY2qE8Pzt\nv/VfIGQAqkWRbmncgrq2lpIk0mxsnWU62ePx40OmszF1FUKvp9czUgl1ZdiZFhyVDUnWRyhJr9cn\ny/qgQwJbSBWevTy+/yeMzBMdmmF+hbfKkGyVkp/5uX8XSUvDBsfy8yIQrBzjuMQT49SRzgoZFKm1\nVkglAqeDCZUToQTZcEg5z0M+wVgc0FhLv+VPUEpRG0u6JFiBajbGG4NTCVGShbDbW5qq4K/97E/9\n/16S/JM9vEcLuez7DqjRkzPuuMTVGQLRPqzOaCwTasu3tYko2t2nNRx+ia7hxE4ql+9dXkBnjNrm\nHOmDMnWHhA5zRGDaXVC44AY6EfQtl0aJTtMx/GZNkGUTWuHacmDQAmwd5K5ngNClGTbabpmd0KDo\nJrF/Iow/sRA9ko4KveNC7ijPnrhb2lzu8pVuiGzliJJOdDYMimsMoi1xPfG8PjCAJw1R957jx338\nQe/98vEprY4vZmngu45DgfIWTA7OIZXACRe8ECXJfEBwRomhpMRjyF2gjEtUHyuDZ9g5cR80Zyef\nvT9x/XgC56EXdJ0a0jukCyXWLsnpupxuu58tb6ODP9MZitboewu+oR9nVE2JbIlYcCF8drMJrqow\nxtMYS9Tr48sSKwVSKyLdMj1JgY502BCkw4oggKxEt3F+4Fl/h+MjYhQCPLRLJkE3QZ+cVMtF2k6i\nll1siTZcfvCJMz/xR9hF25N5fGiQ7NB9ywaX42/tLG2QKVOYzpvxYScNLmCDkmo5wb2nJQxRIJ5U\nPfLOUecL4n6KUC1JB6DTGFubEx7N8XWrtqV4adROuN3duDz5azAAUki0VkG3srVyJ5yiJw1DN4Fb\nryDMZYHQHUCoHb/2+yOllh7atyCbfTd6xz0PHXdSd52+QztKkF4gZSBN6cb2iesSomXatqytnKI/\nWCWvFsRS0I9TJtUEvMXmHqEEXjr6OsZoAuDIB7Rh44I77pEtHE0suTOOPajjsVkGXoJAMCtak2rD\nh7SKMO39dza/855oG+q6nI1vVb+FD6GHckFMqJjnYD1VVWOahrKs2N3dZT6fMNnfo6kbJB6lFFl/\nxGA44qmrT9MbDHFZH6lkaEXHk40yFlNJGWt8KfFGhKY7Lz64LL7t8RExCieWvljOzmX4cHw/xy5d\nJz4qTv6TB5bV6ic/0rnTy12UD+yw3Xd9cAdd+gTtUhMyfO6kt+EqHPGTUYvvXOaT2/rxz0oq/LIQ\nEJJTvlv4oltEx94BJ4Ri8U9e4Qd5lboFefLqj9mL/Mk3sVwRH3ity/Af5wfCAnq8M2W4kp54ByER\n3A6HFMc757FBOL7P5cB3C7AdMyk6PgW+JaQJ7xWMVoasjTbwVGDCbth5jM47ZKrp6UCUsqokRRNR\nO9tiM8I4BTXzE3OnG+vWcxB88Ls9SojQZOdbQuDjJEa4ttBVR+B+6G5VLO9RCMC2oZID4SWmDT0P\nx2P6Tc3D+4+YzybkiwU7j+5SlTnKGySSJMnQSrfU/BXp/ZT1rVOsbGriJAEd4T3UdY1V4KXEVJJI\nHM+Ebwk9v83xkTEKYZ4cT7QuRAjhwLH3EFyw1gq3u30ngtJ5GR2YpWu9FeEnvBMc72Hd13QLm+P1\n0W5hy39yrWcQHPnlIEsRdpw4GQVvwjXESUZhZ7TuxvL/bhF57xFSIJ3A0brKQiK9pLGBKWm5CE+E\nEcfGrh2LE+v7hK04/kF0u3qIX2U76U96QO3In/zkiXOFCT1fNPR6YWE0ZU3a8+TlHDM7pJxOyKcz\nJvsTVKxQqSZOUjbOXqQ3HBLHKVq3KcyWuuzYtWuHub0MpWSXp1zewPJZeIew8MzqecinqJWYh8bg\nCQLEkZShNyBNeJQbZouKza0+cVKQCIW3jlg0KNcgRRwSee3z6BKeodntSc+y2/mlsyjhUVqwqA3e\n6+UYKiFCWVWJ4/2se3a0c9p7mrKiKCsoK+JIM59MmEzm3Lt3m2JRslgcUldF8ETxgEXqQAGIBiMs\nVVNQVDllteDe/Vuc2r7E5tYZBqvrrG+tkkYCYxe4IkW0cys40SpIFHzI4yNjFDp8/PGk6GZ8t/j/\n6CMkq1xbdqN1r9tT0p2rPY8/sUq7s/puqZzwI9t/PQ47T3z7H7GLhdkQXlfOoqXA2uPvFQSX0bXh\nhdJJQEzb9t89SCmXhrHzEzhh7Lr/OsNwUv78eJy6O27/vXvpA+6UePLF5X0fL8LgElvn6KcCV5cU\nZcXRo33SoeboYI+mabBVhfCeyM0p94PytHWOyePHbJ0/z8rGFutbp5eoyS5G/+CQIjhOJvtjg+Cd\nxwkfqhfKk5gKKRSVWaB8jaYhwqKlR2vFIMuYlHMirYkiTao1aSRZlAUraQwy6CA0xuF0jFpu454P\nMjwdD174KUIghaYkgMs6dy0kg5f+2LEXJDuvJ5ytbhryRYWbH+Kbkpt37pHP5+SzKaMzL1EcPMA7\nh/XBa4p0RBproigmy/pY7+mP1igmRyTDPqaqmR/tI6xhfW0FbxrqWU3TGCIZ41XU5iy6tv8Pf3xk\njEKIwdtE2EnX3Lc5gO7hAc6H2Nt5GxIyzgfAU6sOvHTbpQBUO+EdeLv0PjobsfQEQ90MOI4lu924\n8za8lEFmLlzYcufvJohUAuU9aRpUgyMdtbukoKwrpPMgFUpESCRVMUWKUMvP4hSZKqpqgpKCSIek\nWKwlSeRBOBoH1rWaE538+ckVdjJwFOEKAwdhl1M4ESl8yw8s78dai2lqJvt75NMpB7sPcdYgaLD3\nS2IdUdc1QmmMLbH1jGS4gTCBEbmcP+L9Nx5hbUPS63H2mRe5+uwLSBW6/bo8hPdBsapLSgQD6Ja2\nW0iJF4JIeoQ03B3fYjXrIY/m9MpD1lxDoxynV5JghHsJz26u8vDuHq72nF5NqY1ndaBZHemQrNUC\nLzTjylI5sCJe5nW65OKSObp9zSKYNTWrkQy5BdcmPtt4XskgvXIy3JVSLqsMAHGaML63w63Xv4zJ\nD5jNq9BqLhUH0y9RFwdIqUJzWBwhEaRZnyRJ6PX7eG+pyyO0suRH+2gdU3iLEI7773+TtVPniNMY\nvxIzTDKMiyiLOUJFQYzYnpgb3+H4yBiFZajZTpZl3HziXjpXvytjeSHxMpRmtNLQJpBca0g6pxta\n2jVPEIxtdQuDLxHx8QAAIABJREFUMQjnfiKWFWL5/nBBon1JhmrocvEdLzQFWCFweFSkUSrgHyIp\nwFms8NjW3bRmgRajNjnpieOUSEqMtPTiGCUF/SQi0gqlFGmvh3WQ1w11Ry8PbWX2uNryxHA9aR+W\nxiA4IydwBE+8y+OswTZlkKd/eJ/5bIop57imIo4jJIqyKpFKs6gqGm84t3KRRVUg+xnSGszCkwhP\nVQvKvOHBe+9w5txFeoMRkUxaN9svh9rDkmHNuRNGQYSwxzYVrqko9YKdekbfWYTwJJEKIjRScbCo\nuLCxRl17skijpMMKiaQOwCNXY40hdhadDOjLMAYLb4PidbeI292+a5fvfCsDiMYiaY1aN45dgnrp\nKbR+Xns+qWQLV4kppgtmkzE0C2aLkrLYJ+tt4EWEsQ2JDH0Q3jm8BB33kXFMbT3poMcgy2iqHD/x\nIDXWSeaLnP5ghJpOGYkRykTYyGGcpTAW5ULo7MyfQd2HY7BL68ovS2jd3HVYF1p3jQ3CqlqEOraz\nBucbIgVZpFgb9UhijdRBQSgvChbTGV5BpQMHf9MYvHMsBUt9h+zrLqhNR7bue1h4Atdaqu5tUhzv\nekJIamcZJQN6mSWlZrSyQZYkLCZzDqZjpvM5xnsaUyIVqDglUY61FU/kJdvrayBihA8ZbxVJ0iRD\nSEVeVVSNY+9oQmMdFS1eiA9YhS7B1JY6l/H5iVj9ZC2+m9zWWcrZEQ/v3GXn0X2K2RHCO+qqZjAc\nMJ0umC2mpNkAJKwMh0RWU6GJ+n28NXgl8apCGEscCZz1NGXBH3z+Nzhz6Qof/+SnUSpCLkOsNlHI\n8c/LsM46nINMJaRCIBYTEJ7cSHAKJ2C0OiBO+jz97BWsK3m8c53VlYRxXuLxnN4YoVTL0NzUaKGJ\npGWrHzPJK3xZkntJYzVCKLSOnqzKtMbJCkXV2LYECda7QCArVUskK469BcJGpVq8gRCCYb9PkmkW\n8wlCWAQOSRbIUygYpBlxrIiiiCTSOGvQPke5hlgluGIBwpKphOFT2xw8HKM8pMMh48NDqrpGK40Q\nmgyDIeBdjDWYxmJN86HX4kfIKPiWq/N49+0SN2HDbju9vAvgDqFQXmKMJdUC6R2ntlY5c3adZ54+\nRz9LGa0OMU5wf+chb7xxncneGJE3WCnx7lh7UYnjZXP85WGiChESlN2O3IWhT5R4ujo7IKRmMR3T\nTyJGVFy7dI4slty801DZmNrGYccvajyCui7pjwZUpWE4TEligRWWUbaKqxyNszhnUEKQaAXWspKl\nLKoaYxs8xyCuE4HPMmvSBbYdQqEzsycTYd3/QsD+3h67u48o8ymmqXDGoFSMd56yahAIsl4Sxss7\nlNKIKqKq5sjY4pAMVhKmO1Osh8PiAYk+Tewcj+/fZnz5EmvrmwiVLmnHu13XnyDO7aoKABqP9hKn\nM0axpJnNSVaGDAY9Iq3Q6SqDWHN0dMDmSh/rDak3SCVIYomxnkRLvI+Zl0col5EJxantdSY37qOd\npBEJXnq8VyfG8YSz6AmCLktj4ZcYANmNsVAIBB1v17JLVIDqCIIJQodlsSDWEaPBEJ2lxDL038SR\nZtTLwHvWN7cCTsU6nIRZUdA4Q98a0r5id3cXaxvSrE/VlEzmR5xa3aCxYETITVTGBH1R92ctfBAs\nxTOWbljnXuLbvIJDqWAMnA9Jub6yyNTx8sev8emXP87HXrhGXi0YP36MtY5+P8U2ggGnuTTaYpGX\n/P6rX+fwcJ9URzihyZsWG+D8ctlAGyq0SQfXIdWcR51YfAiW4jFaKaQPLEArOufa05e5svUCC+tQ\nSpD1BYNCUlYxsVb04xhHaNlNotDfX+ULCjVCK4GRNYMsQ+qUo8kUGzA6pLEOsOwkQpAzrxryJqgs\niQ7CfILPQJ5IXHaTXHDC7RWijfMdOMvtm28zPzzANhV1VQeAjlgwnzrWspTT/ZSkLNEqZioSjIZZ\n9YAsTSjmBWU9ppdsIns9ECVr7hKVa3i4dx/rPbfu3mb97Br/2k/9WyilMEvNxhZFSXDnvXNoEVCE\nVb4ALVmJExrg1Plthiur0FTkjWV9KEiiI9xaj3UyJmXNha1VTFWjIk1TO85d6HH3/SlJsobFs3M4\npRkfcnptyP5kTlnlWBlj2vHo2LTa6BHwLHz3/ANGRfgK20wpUSiRouMhQobmNqEcgobIj3A+tPxX\nRY5UCePpI/78Z3+A1fV1hitD4n6P9XQVrwVCumXi1AooygrbFEQ6ZlqW7OzsM1vMGAxOYRmyKKbc\n23+TlZWrqFnEZl3gsxVqWzNvKrIoQkgfVKg/5PHRMAqEDG5AE7e7WRtNOOHoAk6pVHDzrSGSMEw1\n22e3+bEf/iz9rM/Nm++zf3hIMZkTSUlv1CdSmqooUVGC1oorVy7S66e8f+8hVVW3JaUI6/xxnAtL\n6Gp7FcvA13u5TDe41tVdJpVEgESfO7PN5e1N1pTFV5796YR8fkRdzOjFGdZAg6c/3GA6eYzFEccp\ncRSTxREi6BgxKysGoy1gTtM0KCVDndwHPYFYSxKnmTY26DJ3lZcOgNEFX8c52ifG/GSewRMYjsv5\njGI+Y14cMUjWMM4xXUyxpgJ3isKIwAkoIfWGRMeICpqqoadTmnqFsq7xTpOuDZgUj9Fa00tGAexF\nhJtHNHWJjtJjL911AXxICOMcToUGLSVBtwszSzJ0HOGwHJaezVEKUjBIUwYyobaGfpIQ4en3+zTe\n0dQNqoi4enYryBEbR6oS8qLg3uGcYRIxXUzxlKDisM+3Gh/LASKIm2jX9WV46nyPeXlEurKJV3OS\nOOSJpI/xaoaPBf8vdW8WY1l+3/d9/tvZ7lp7dfU23c0ZchZSpEhJlBzJkhUHtmJbMaDEcV4sI4Dy\nkLzHT8mrHoIABgIEEJBNARLH2RQbjiIZirXAsjaSmiE5w9m33qvq1t3P8t/y8D9V04wUaQwoBnWA\nQjdu3+p769Y5v/P7/b6bEwu2px354ACQWO+ZDCZ89qWXGI/HVEUGUpAZgxICrTXRW3wMKGOYDIac\nnj5CyMjR3hSCYfHglNyMGZl9DODkSyzP3iHXGc12jSpaitEIKZOwjR4G/7TH90RRSBdhOnOvgppi\n2ivIFAUNQiJ7LDsjkonAy194ni9+3z32j3c4fXTGk9deQ+mMIkRMJrFnHY0LCKNYs2UwGrG7s4vR\nGUEK5osNpxdrEklR4GPs7/xJo/AJXbpvBRG9+9An71sIQSDdpY2SCNdw4/oRJ5ViWVts29DMHxHb\nLcPK4L1GZArvDWUpkF06kT0glMRH0NrgnWXlaqyPjCZTFqdPabuOLnqqvCCGgBKR0kiaekueZUil\nwbtnYN3LRZ54hsdxOS48C7ql51tnWa83SCEYVTu0TcuqWdKKNQhB5y2+CdgQ0c4RvSDLM1T0RGfJ\nqpLKFZxuniBFRThzqEHO5mJNEBoRHdE32K1nfjFjZ+8QKdXVPuMTECUtg1frNVoZ8kwwMQWL7YZq\nWFH7wHZVc+1gyvFkiMaTVzlaBjZWUkTBZ259ht1hwXtvvkEMken+HuVgRFs31PWWwWLDartmWXe4\n0KF1Rmfb3tVJpOXysyvaHq4KfCJrfvCN32Z452VGN09QZGRF3tPKBXXjCdHjtEWW/TIzWqaDkuP9\nm0x3dyjLEdNhgZaCGH1/PklCzIghJD1IgDKrqG1L1wh2jyYMlwfYNu039HCfUbtF79zFNU+I4TOI\naCmm+4iZpQkNKHlF2Po0x/dGUbicuWJCD/pbVz9WKKRMUKMWUGaGa9MpLz5/jx/4/peptxte/a3f\nY7OaM83GlEVOoQQ6FyiTnJuscyw2De3iHMoxwTu++uWXWG9b3njnjMdnc5wuyEZj/HaLt5bV2eN0\nwYdkGhpDRIaQlmNCXBUESYIqdTmgxFJVA04mOZmJUHuePH4HlU053BPsDid86bMvMBoOUD4tTM8u\nZsznS87Oz3n38SlbH3HBEtAUWca22bAVA3Q+oOnOybWiaVukkJSZxm0adoYl6yaFliISWxK4KgLy\niknYM+yeGX/SEjXineXi/AwXkoHIZnPBTrHHIC+oNyWOQFfX6fdAwEdDExw6Nnzh4BgkvP7oAV5n\nqXDEFbYsUN2QLB/Szk8JPrDxDjPU/Oav/hO++MM/xs3b97gUwUX6zTsRHz3DLGOYae4e5uwqjcp3\n2QhLqDoKMWRnZ4jzjhuHyb8wruHecxO0yahMwXB8yPgLBiEEOpMonbFZrLDdkGm5YLHKaTY1uVpz\nvq6xQdP4jhgDUqauJuV/9Gva4JEGvHV01nHj+3+aqA2xy/BSpbg6IsZkqJATLloGezeJQ8NqMQO3\n5dbxMbdu3mY4mlJWA8oyS/HxnzC3CDEF3woEzlnGwwll13K6WtGce66NrvH+5gFKbrAhkO+fIJcz\n1HDMpl4ylZJu/pTgHZlKNviXWsJPc3xPFIXvgnIuSUyXUMDlJj0EhoOMUaX56ve/wu2bt2hWa5YX\nMxanFh0GXH/lJkbnCJOYjM5abIyI4BhnOdvNFj8YsVmu2KwcIUiGVc614wPuPzpl87RGymTrpU0f\nnuE9l/xJSAarlzRepCAE+pYxYIxkVCkKJdLYEiwqS23z0WTKZ65f4/te/ixFUSIDuK5lsz7Cto7N\n+YzX3n6LVz/8iG1nmW1ahM7QQL05ZbxziPcJrlNXWL/AaEWpJJ2S2Kv05k/uCs/yEcR3dQ/9z9Nv\n/p3zPHn6mHVYI9uaEBpObh5irWN96nFdS2ctIYL0HSFYBDnBBWbthuvDHSZGM3cNeRB0MdLMV2Ql\niKDI8gLrIoNBpJgWrO5f8N5b32H/8AipDEabq8WjlJqIp/aOKkYKY9iEwFAJ9ssRk7KiykumWUEk\nFcGugTvPHVJWOSY3ZEWFyg15sZ/GquAIwSXoOhPkpqbMLZOyYLltGRjNprE0zoHuIwCDSqPC5QcW\nY1LBOofrOlarLUIqNudbZB7YOTrBKIlEEUOFUDn5eMBm0eGCp+taTK6pyoJcCYyIiB7FkPJSSwJC\nKCCCKhBBYOslIfbZm8KTK8XhcMrFYoXEYZu0vAzOMxjuInrzG6lU/1sWfw7HBwFG6/5Dh0tG3WWn\nkKtIpRRf+NwxX/zSS9y8exPbWN77xlsUpuArf+mrDPf3GQ0zlAQXLJ1zbNcrYtfi25blxw+IInJx\nccZIC5rZGYvNFtuB95GD/V0skrbZ0iwXTKdjbNPQOUfTpQ3wJcRnVMoRdA5iDAkKCh03pkPuHe5y\nczKhdpbFfEW3eYzJ9/iB51/k9s1b7Iz7gFVtiMEzmIwJLsLhTe7deYUvPHyPDz68z7/45h8w37RE\nArO6oW0/JDcqxaX34w3BUhhFrgS5FLQhXJ3AWuqezks/q4srXccnuxK4hE1c51gulohGM9S77E6H\nZCEyyQ2HRztUxQA1mODbDXpQIlUgMwWubjhfPuWh9fyFH3iR9+6f8Xi1ZF3X+CB4uqrR05LlkzUt\nlnbRsV5tiI3n47ff5rk7d5juHSGGoyvJ9yXXZJJJcgXLRcNgUFAZyHPF8d0jwkVLOchYrh3HkxHV\ntYLBYERxeCvtILICZKI4EzyhbQiuwVuH9oE2z7HOsjMc0LlA9XRBrltil2TwUXgSonApOAIRkjDO\nh4BQkuW2Q5uccjxFCE+9rWmVQrcuEY9KRTaMtFtBU29wBPZ29tjdmSAJiOAJvhcCCnW10PbeJVm+\n9cneLgicDxiZY8YTlssFu5MprQ3I1iFyTXQFbb1kcuMGk/EYOgfakNK5wX361LjvnaKgteaS4x4v\nV75CkJuM0ggKBS++eI+DvV2Cbzk/e4KUGVlZUe5NyEuDkaGXOSdzUW8toW2pZxfY1YpgO+JmQ7Ae\nVY0oy5LZxRNckNSxI2hDJoFMoXINwRBFpHOOGEWvwYjPLPJS16BERCvB3qSkyBV127JstjT1luA8\nMoPd6ZiyyNKdW/abPyFBGpCeID1RCw6n++wdfZa377+HdefUrmGaZyyalk0MlEYjiDS2QwvQymCU\nSHMpEa7INRGlDVzOxT399o9OlumxS3ptDBGlJNYH8izHaChyz6AcoosMipS0pJRCKIUYDCkGQ77+\n7W/wyp3Pc3yiERcFr7/5BkU1Zf94j6a9IJnsKuy6w3uPVopGNDRNnZyU+ySpy7cqpYB+T1NWBTJs\n2bSOyd4eXdORRehsJAuaPDPkJiMb7iBdi8xzhO5bfxGSptknt2epNDEkaFVL2Xs6arTsxW6XyJdI\n3Wm8BMMg/R8yEaxCCDy92IAJjGPLsNBoE9AxEn3AFBVaSIJTBGxaSUcYVRWDskifnRRX2HsU9EYu\n8QqFk1ISQ+pGgw/JQ7Jep8cRlOUA6zb4piF6R5EVNBcLvMpRWQZCIGMv6/n0jcL3RlGQUjIcZEDK\nEkhhmklwNC4NeRZ5/vYxJzeP0YVjfjpjdX7GOIOh9NTvvkZX7XNejhJjcHmOJqC9Q0WP6CyWkNxw\nbBKlPHn4kDbAYmMTLJmNkFnOye0bwIAmehoZWTfQOY/AE6TDKHXFyLvU5o9yyajK2R9qDIEuOp4+\n+gBX1zRbx/H+mMO9Pcq8gCjBeYS+bEkhAdkQTMDLFrv+gJfv3kEjiOGMVdfw3I0X+fDjN6i9pcgN\nzlkmoxExRoyVZEoi6Rd2KpnA+rruu97IFSAl6ENPE1wpRdJkBO/xIYIPhCgosoxxkdN2LdPxASYr\nyfQAZQQIm1h6VYXJRuRacuf6bXzXUImCKh9wtHvA47NzfvsPX2O53rKqWzofaOoG7wNSKTrrEuwp\nRFrsBn9lB5dJwf4gY5RLmu2a4+Njjvd3KPIcXUeGecbE5OwdDChMhskKZLCIy1M6WCKS2HZE15GW\n2Jf6miRwMlKRZYY8y9C9m5HqdQyBxO5MhKRezh3TOWR7P8cPzlbUNPB0w/54wI39AlGW7E0qTmyX\n9C5dTggBbTJKYxiVhiLP+07BodDI3ofyEoqP0fcUk2RdX07GRO8R6zld09J2HucdRkSqImfddzfD\n4YCwfIswHlBUg35f5BERvP9zNz4IlNJIEdECgop9rLskUzCqMsajkrZrQEXmZ6ecn58xHU8xWY4X\nqZXWm4tkhuE2EJKE1jtLW9ds65a2SWEbddvRNltq66i3Huc9MSqMECzWa0wm0ZUmH5RYIZGbGiUh\nNwZ9qTbrF59aQOs6Tqop8/kCPZ3ivEXqMo0xoUixX1omS3GVIVQaixJNOeUNCKXAOWTvrFzmht3J\nmLPFBrnYcn76HqPxPsvlGdlgQBUjIkQGEboro4949fWs09F3Samudg79rgYgRrRSVNUAJdOWIc8M\nMkoKXaCUIdMGpVMBlyIZgRamTAa7RhNF2nUIAiE48qgYDioEgcxo7GKD9fEKIgs+4IVL+g6T9xdD\nsqfTWjHQhp2hweAYTnYojGDb1BgjeNIuObh1l51hTiZTex+RfQSg7hEAhVAGv2nAgTIqyauVRopk\n+yeVwSlNJwQuJpmskepKuHZJl38GFunlz6FvyR1dSD9L0yWT36ERqBjw/fd1tce7VBS0NNDTntso\naT2Y/rVib7DiQ0wISOwLVG5wNnVRWTkmxg2yWyN9JFMSrwKZlniRPvvB7l2kVp+wVbnscD/98T1R\nFJIZSIZR6cQOIeJjYhrmeWRYGexmzqO3A7s3T6jPNzx40vDgrW+T5wJspF4sQIIOjhdu36YqCz74\n6D6niw2LTc1yvUIA4/EUJWE+n+OAi+YcVUxpF0t0XmKKlDkxyXYQQmN0miWliLjQYaWiSKA5uVRo\nKbh1dEAWOyw1bawp5ZiTgxPun9cYU1JUFYvlhvbRDKtyBtM9dnan5MMxvpvjN458WOKlJwqBC5IG\nSaMVrjAMC81i01AVU1yzgLZjf2fEdr3BCskwN3hpeLxY9XeYdFGE4AgxUXIV/ZLMJ5KYNiplJggI\nvqHMBPfu3eP3f+v/YiyHaJOxbmoyrVnULZu2Y1AWSYEok8uPiwLLGlmUBCI6OozWlCanKCqmMfD5\n52/x+nfeBecILhBc4gRmWVqcTcfpc770U7y9MyHLNYNS8MrxhOg9e7eu8/DDRxQKSiE4unaL3WqA\nlJouCN4/dxSDFTt1R16NQShitiHTikmRI5Ui2CblOATJykZe/fApD85mhC6FEPsIQUS6GFKR6YVh\nSWMTeg5NSGayPW3F5BrjI7rKKIYpsFipFO7mg8fbju0WtusFwbUMx0PqoHj8dMG2/oAQBNPBkMFg\nwHhQXrEgQwg47zk/33L2dIkTG7RWjIcDsjxDCoVvG0xuCBpyGchyxag0fG7vAFkOWCqJD7HPrbzi\n2H2q43uiKMR4aZbBFeU2xh7ndR7bBtbCMckK6qdnXMwX6LZlVJb4IPjg9CFhu2K2OGVkFJmQ3Njf\n41tvvMmqs9gIq8aSmYzGgVaR9WpBRNHKivr0XYaTz9DWK0LXEo1hs6gpigmZ6clLAYiCznnGuQYh\nmWoDUjItM+qNJQrDqrHYrsFGne7O0pCZnLaF+dby5v33qLcNe+MRt6/f4ss/+mNY/5AYSxof+M6b\nb/Pu/ce8+/EDopJUhWF3UCFj5PTiA4SEtuvI5ABDag8LZbAq/4Tr0TPyQrgUwVyqDwXeSSQBL32/\nxwG7PcVtWzJZIvKKNkoW25aHD87pnEOFwM6o4rO3TxhUBVoaWmv5+Mkp67pmtU2elNWwZH9/l+dP\nDhmXJSbX7O3sMhk9RYlZv3+BTCXL8rLIEkpg0ugohOD6wYgudgQXuHlQ0npFUQ0ZDIbgWoJUGKmI\nQrCcN5QDQ41hJx/ReM3s4VOezmZsm45sUPKDL7/AqMoZGEVXWx4/fsx7773Px6czJlXFeOeEx8sL\n/P0EQRspUshN+MRVKvlwpC7nsigIAWWZYRtHMcipjMIoBSIpO7XkipAXfcB7T9AS3zq87MDCZrPh\n/PETijzjhXt3GJYFQsBqseJivuA77z3E4ZmMd5iOx7SdRUTPcLLLSAla22K47NA8UgkeNy0Khc5T\nZklPyv0uROpPO/7UoiCE+K+BvwY8jTG+0j+2C/xPwHPAB8C/E2O8EKln+fvATwFb4GdjjF//U18D\nSaHBaEFLCQS6ep7MTeloN4ELA3a9BBKq0NYbvvXe+yyWa557/jkaNWe6f8TZhx/wY1+9w4t3nuN/\n+bXf4Hy+QEjFYPcmLgbm9RmLp3N89AQhKUc7RD9hdv6Yohjz5IPvMJ4eMDq+Qb1ZYbT+xMBUKgiB\n546mqAg2JFjzdL1BBcvBdMxivSBEyareokTk+HjKjRtH/N43vsHr731Ma4Y8ns25Pi55+uiCW3dv\nMC1LTu+/zxvvvsW33nyLRe15aiWzJzOss+TBMTAKLQTRDCkpoA40LjAtM2ywIEKama92mJfKzl6x\nFwRCpoJ4adiCSLDVaOc5Hnevcj47ZVDsILWmdpaziwWd86w6izq/YHYx4+bRPj/wA3+BdnmOXV2w\nWq55UidV3ovFMXYxZzvOqJs1e+MRN66dIFTBtz56Qmwst/amnOSaB21Lsb/PYLKTjEtlw6L2fPP9\nh5SDkoNRTh0qdg93kSbncy9/lo/ffB27aimNphARfbSPjJHlasVv/N77/OLvfY3be0ccFAN+7Plb\n7ArJajYjF/toBPc/vs+rb33MaeP59mnk+ed3+Yf/6y+hjOTk7m3mFwuUNvi2X3wGUtcaYton9LuY\nS57HzrhkspOze7iPJjAtTd9NKUyRlrw2RMrRlLiaU5gxu5Wk3dQ8ePARGxt4MptR5SWDIuPw6JDD\n42usTs9ZrGumeydstmveu/8B+MD1nT2qMuf4lqAsc8rhAUppgniExzPIM2J7gXUeoZNhbewrmPgz\n7hT+W+C/AH7xmcf+HvBrz4TI/j3gPwb+KvB8//VDwH/Z//knH/2bTnsulxY8Idlt+eBwMdB6z7mv\nMQqc61hu16xDIJQj5hceHQY0ds5otMewKNnML7h2fJgyD6Mm+kAMHV29ScEhKBQS6xzBB6xrUGis\nhFobRv4aOit6WbZMseC9VqDqE3sqk9EETb14SpYpxhONbRSnjWc+34CUGC0Yljm70ymT8SOKkxfI\nxk95fndELiD6DvSUOtQsljMiksmwos0Dm/WCQZFzvpzTdB2lMXi3YYMlsx1DlZKe5pst1XDcdwqh\n53o8s0gktbWqX6DxXYzHVDR2ywOK/REFrzMcZIwN+PGwT8dSDIoMu5mD1OztjAHP4cEJZbnhcybj\nft1y++QwqVRHE7Iio6wqTKmYTKaMygLn4WRvyp6WLFZLhpNdTGbwIfD+G2/znYcfcm06YphrVLTs\nGMnibMGNF25x43Nf4Py9dzm3DZnIyDJDkSukzlncn/M7b33Ev/Hlr1LJSFHscvCZF1EXb1OUFZGI\nC5LOdty+dZNjVVFvf49qlfHSZ5/n+edOePvBQ3ZMTh36pWxMIqIYYs9GTFZqIYZkhy8EuVFIYxho\nhY6BUksymVCNS1nVJyFGgmowYjQUTMoB0jd0oWCys2JS5QyGinI0YjCagnhINdjFUJBlA7oQadsN\nw3EK0KmKLMUqelBaUugk09dKEcoxZEO0Nj2P5tKN+89w0Rhj/E0hxHP/r4d/Gvjx/u//HfDrpKLw\n08AvxtSr/I4QYnoZS/8nvUYIgcZD9BHraggOZ12Cfry7WkrJriYzySNfZkvu3RlQ5XvAiDwruXvt\nJlWZUyLotORgfxcRQKuMoixpo0cXn2EzWzCbbbDScrpYYv0K5wJNvQUR0MKxWs+oRnsIWSXnUimT\nW7PsnZCcp2sbiCVBJsu2i7MFUsJHH77BcHqNrnNMqgGDTPGXfvKH+PGf+BHW64RBKxXIioqqyjCZ\nAueRPvLXf+JHqINJtuX+83RtzWazZLFc8tuvvcfDuqNqLbX0KK1QBO7uTFk5Qa4lrfeEYFEyJVQp\n6M1KI8g+noze4u5S6acE5fQ6xnt+9K/8BB+9/k0m2nJw7yZdr7LTRNy45ODokKO9I3KdcfP4Ji5o\n/DDny9EQuEKEAAAgAElEQVQjRI70K4amgODw7Yb16oLQtOxPdri2X3Dn2i7KbnHXTrj9xR+mzAvq\n1Zwn5yvExX0yU3AygbGs+PaHT8mHAz5/7S4XH7zDvS99ierNt9gsLNPKMBKBalDxV3/0B/krf/Er\nOJHhnYIgiG5JdueLuNUFgoDqDIN8iJYwGBpu/OgX8XaLVj9OW3e88fEDBoXB1h14R7AuhcDEZFBy\n6bQdY+/nLAQ7ucQTGOLJtKbSyTlaQC+wIxWEEIlBocwuMttwtDfgaGdM3TmctWgFxEAxHBF9ZFgO\nGA4NxlTECHebMRCZTgdkRuGdp7Udq+0C2wiUSKG+IYLSJWQVsiqRSuNWc0KI6H8Fgqijyws9xvhI\nCHHYP34d+PiZ593vH/sTiwKkJVPgE6fmyy15vOQcEHDRI2OgzBUHw9tUOrI/nbK3dxsps2SD1ja4\n3kl3Np+zaWtG44LxzoDVcoZvFuTSMSrLlCEpNAhNDA4vks8CPbHIB48NHt9vhkPPAHQ4pE5S3xgb\njIvkuaHtOoie3dERQahkvimmRBcpzACkpig03oeky1cKIQMCRWcDKsvQ0XBvdx+vIl3T4VvPxcVT\nzqRif++Y9ZOavRvP8e57v82s6bg5yNFE9vL8is4ciYRgkVr1ar5U1BCX+Q+XM0Zqi4k9n18I9o4O\nePxuhrMte2VOJE8MPe/YrrccTPdROqMsKiIOYwSrBeCA7AKpJcQ2GcF4n1ilXctkNKIqhhSZYbF1\n7N66ye7RCaMsY3cw4M3p+9SzjEfbM3IzQI4ieQioGHn7m99gtxpw8rmXWHz8iG2wWB8JNhI7hxln\nEFtMmYErkNWU0BXgG8JagY9EZTEmY7M9R1WK3d1diFO250u6aPEubfx9CEgpmEzGtLN0QV0hEPEZ\nvUhMBrCxp+grKXpfhcunfiIuCUDUhv3DKc3TU1xRMMpy8rzE2ySnXq0WaKGJIZIbjVQKk0mM0cSd\nQ4SPlEZAiGzaNcF7RJ8oJXr0qtAGOd7BogjeJpFZ6M2E+DPsFP4ljz/ulf/YaUYI8XPAzwEMJ7u9\nm1JvtCpA9EKolE2YMiQ7HyAE8qLgqBxw4+CIwWBAOZoinOfj997nO++8zdsffoQPsJydUxYK5QMv\nf+n7OB/ucv/++1gT2MYlsetwdo3vWoTUmHxEmWvq1jHpOe8+JBLUlfV7iNjO0npLUzc09ZbhYMi6\n25IZ2KzPOTq6jm0ayqygaxu6tkN0Fqk1JjdEZemaHiorM/AWHSVVMeDdj+5TZhU3dncQpcHqDr+q\nqIXmyGz4wD3lS7e+xCvX/y1+5V/8ErnWBCU579rEhepJPxKFcx0h0wgfGORJMCX71GXR+1PUtevR\nniTGmezucvdzL/Hht76GiR6tDdE7ms5y7eQa+8cnNFuJiGPa1UPa7px89xhXB3KT+BbWNnRNjbWW\npvW0znPzxjWKIqdez3FFxQsvfT86L4giIS5f+cGvYF95kV/4+z/Po/WW2Qbu7lRkwxGvv/EOd2/d\nYDR5yM2XX+F3/9k/x+DZrYYYvaVq56hyjCrH/em2Jghw3iIA39SIvRHleMzDRw/ZnRZkqiQ6i1Sa\npnWsNs1VkCtScrFa97Z3IoUU0d+oiCAVWZUxyDNQBcNck+lk6iOVTtTr3gbw8g6nVYasFKsusp6v\nyIZDlNYpvbttyTKNxtFuLNF5bIDo1uSjMZmaIrC09RJnG7bbDdZ7to1NvBSTXjeonLzegNQs68uO\n1PTBv///p04/uRwLhBDXgKf94/eBm8887wbw8I/7D2KMvwD8AsDhya2YyCVJFnzpCpTkRjKhE+GS\naCb7f5cYnaGEQXjwzlOOx1y7cZNyMGU5O+dN15HFSCklG9ERjKDIUjZlIJF0jJJsvUNpQXQbBvtH\nbNsOlEl3CWt7nYq4KlxN29JYS1vPCU7TNS15rrmYP8QE6BpLsoBT1E3LarMhuA4RVbp7+o7NYo4p\nS3S+mwQ7Os2iy+WMh48fcHNcJj4AhoAiunT3yLVktlpwZDSZ1DTWkwlBrnTPxkuFq4sdMXq+/vVf\nZlQVCH2brol4YVHSk6s1o9E+148+g5YZPrTJ0CMGBtMpMsvxXY20yeSl6TrGwbCeL8myEdZ3DCfX\nWN5/ndd/61d44eUvotWQ4CK2benaBh8DNqQvnRvWXctsvQQhaZuWfDjCO5CKRCASIqk9RcC1LQsb\n6E5nTEcFDx4/xXaBYXmfYZlR5Bk2OKxzeOuRxiKqm8RugV89wa3mOFsTfTJOkSZDGYcNgYvZnKKs\nED7SbhuWyxXeeUwfCix792vvwyf2cPCMkjoSvCU3iXegRLz6viutWR/uk3jLDqVyIoLWOpZbz0Ab\nlOiI3tF2LXlVJlTDueRg5Tzbek1oa6a7ARE9tt1iraVr2yShD4kVm97/FnTLyGU4J+gI5HlBi09F\n4V9BFP0/Av4O8PP9n//HM4//R0KIf0BaMC7+tH3C5XFl597z9EXvZxBi6BVkl641KUTjUlQmIolh\n5gPT/RNMucudgw2ma9grR1wsZkz2JhiXU0hFtTMmrgXuySnWdQQhU3WPHRrBYj6nmuwgVZ6Yfu4Z\nKXLfPl56LzjfgCjZH02Zry/oupZquIMWyb3Xx8h6U3M2u8A7SwwC5zxNu2G5XlNJiJstRTVARE8m\nU4f08PQhm4M9hMrZhMCmW1CHOrEQpWDTdswzm+5YRmJDSAIy0UNnvcIO4OzNR5xNKkJ0NJvAYKdA\nahjuSmabR5ydLtk/HHNt9znQ4INHZAZdVjT1At9z/WvrePPdt5iePqL66F0yrRnojOHuAUfHN+ma\nhqLI8cHTbre4nqXnenbqbLPi7be/gxJQTnf54K33qJ503Hv5BEdEm8QaLAYFUkWCztAmMTebNuJs\ny3p2Rucz/vJXv0QRAhfrltXKM5zuIdoGTr+Dc47HT87YnSRDVu8sGEXMcnzX0TrPYrngxsEtbHCs\ntytmyxVIhckMVZ6xqbueqBT6G4H4I+dqiJK8yK98QlGC8ExRQJBk4VFcZXNGIfAeWu+p6y0ScM7i\nYiTIhBi5tsXXK2JQWNsRQsT0eadN1+KDp3P2SqgVRWR3f48QJU+WW+pVi7WB4f6Ytq4T2xFB+2eZ\nOi2E+B9JS8V9IcR94D8lFYN/KIT494GPgH+7f/r/SYIj3yFBkn/3U70LkSqzFH2SjYykWPGYshbE\n5ViRqm8nVGIaCoH1HdX0GFfXuLqm26z48MET5uenbDZrhNZ461k2M6KMBLbUA8dgMqVxc2S94T/4\n23+L/+p//gdIt2T/6B7ZcJIqsbN4Hwk+iWP6KkVrE0tNqQlSSl798H32RyXrdSTPBXuAkoFhmbF1\nHR8+fMiq3oJd8PDJGcpobn7mHkVZsl23PPzglK+/fx/pOq5PpshM8Y2PPsRIwbWyQK47los5LiqM\nNvzuO68xEZ6LeYMdZjx3MGHdCUpjWMqWGCRaZnS+4d/86b/BYLRHjAqPIuY5D2Zzzi5mxKhZrbY8\nONtilw/RakWxu0NBzrWb93jj/e8wNBmdkNgoeTw75/7TM25cP+Zw74i7h9cxwxFPjMHKSOsc3js6\n1+F8wHmLExCU4O333sHpgp/4mZ/l6OCYTesYj8qru6n3iZ49my2pSk1RJKdBGSxdEEglIMtpN2e8\nc/8Rr9y5zaQs6FxgtVzgfUHcrhFZxsFeRTacpPvIgaFdLnnnm2/hveVw/4g8h/P6FKJnvlrw4eyC\noDXSSLLMoM2lYvGPmYZF8lmwTdcX4UB0DgckWkjKJbHKo5Qmr3JEOSBZcXucHnK+fIKxNQRPNhii\njeFstWHz9Jy67sgUTCf7nIz3WUbL2eqc6G3aIcRAECIll1cjtu2G1WxOPhhyMN3hD998h7LM2Pid\nRBbLK+qm46OL1ae6FOHToQ9/+//jn37yj3luBP7DT/3qzxyXgSXJiFRcwX9KSkAh5aX1u6ezDhfo\nC0WgW8+xzZbf+Z1vsZmtWczmaFqq4Yj3P36TUTXhjraM9oZsW4PbWtr1CikEN46f47nr1wmdY3/3\nhKwoUdnl0i7p+kPw/SiTFnTRQdd6lIrYpsF2DbgchWS1WLNzcsTWRmoXybTGNh0+JledQVUgtCQG\ng3eQCc2oGvDFG9cQLkC9YU1gIWuciFzEyEZGrIxo2YfM2gZdZCilWW9aur1I7KnOsl+yhp4MJrMp\nvvN0ncMKwUDn3N7Z4bm9fbwLbJsWFyK+3RLiMZvFh1z4Weq+tGHVejrfIrIBh7v77JcFJ6Mhe2XB\nMM9YdA11O4fOUYwmuBjTV0hpSi54Ou/ZBs/u0R2Ge8d4oxnnqg9ikVewn+wFUM56tq5mslMwHFZ0\nXYsjMspyZLGD6yyrzrI/GVJlKmUnxtSip65P4R2JeRgsUuecnFzDdS3nTx9hvSJKR3AdF51n0brk\nwE2KK1dKolQfvnOpTfmu8zw9tlg3RCkYlBpDJMpLb0aXMkUjiE4lyDuQltimoHUpM1OgsL1/Q17k\nSGMoKk8WU7rT1nmCb3C26cOV+/FQpXAeGNK4FU2U6HKEdbBZbdjML/ihf+1lzpcLbJtSo/4lwIfv\nDUajSN1X6hT6X0SmJCEqokjwFtFBbCEIXPQs6w2zbsvEDFmefsy78/t85s4tss9VxEWaaWePnjAw\nhm23oW4cbraibS3NeovB09RLfu7f/VlGxwOGaoCuMnQxAG3IywrbtsQQrnjxxLT36KJDFQrXNpyt\nNjxZ1hiVM50cMH9yn1VTc3dvwserNVU5ZH66Yl5b9ndGHN/Zp9ms+db//btcu3uPg5sHFKNDpqOG\nSirq7YpXf+8PsCry+Rfv8PKdL/Abv//PMFnGvGlprGOSa6QS3Ls+ZLauWVnLIFdkKtGuGxfwsSNG\naDqPNxpVZBhtONtuIIZkQyc+yciQmUTGyHT3NjFGOtfxtvwDbKjJhKRbzqlXDdbkrGYbMq05PJsj\njEGNKy7mc/J7BUo7tm3bz+KRRVNjAxwe32B8/YS8ylEiPmPUKlIn2DtdnV/MyY2iMpq1cQzGe+SZ\nZpxJZBD46BlNcv7Rb/w6f/ev/ww71w/IuhXCaExZgJB0qwXv/ParlFlGlpcgJc26pmtb3nz3TQaH\nuzz/5efphOStpxe88+gxxWhIZyNtjHSdp7F99iNXv3qAK0l/iPCf//x/gtQZWTGgqiqeu/MC+wdH\n7EynHJ9cR2mDlOD7PUDbdLz73gfQLDnZucGoyDHO025XPH1wRlcnN6sgPEKCDRZtFHsn+5RVRefS\nrsI7i3cdi/N3WNYNGzXl4uE5h/vH3LxzgzdefYv1kxnjnQNCnLGOlts71ae+Hr8nikLCJy7n4P6E\nESlcJSsGBGshWlyb4EHnHXXbcbGaM8kMKnruHt1gpCdsly0Pn9xnMb/ARUExyRhIQdAanO9Veh7r\nPK21DItA025xmaY1OVXvEB287a3XPglJjT0MZb0l+nQCn64apoMh3keatgNTogtDIzU6UwihyIYl\n7737PqMvvYJqa7Ii54WvfBa7dbimg7BiMJhSX6z59lvvA4LjowNMPuH+/Y9Ytx1155jVDbUP7O1d\np109ZlJmCC15NN8wyjLMpa248AmGFB7Rm6+s1y1IRZCXpq2ScBmK2f9cl9vyEANKG6rBiPP5nOnh\nEcNhpGPGOC+5trNHkWWM9sbETCMygSkFtd2ihaZxiWMSve/xuMj3/8Tf4PiFLyCaJSL6q26Qq84w\nLZC9TxkZSmuk0CzO1+yMB9QCqiyjixtGVclP/chXeOuDd/j8wDAqAphJMsTJFMVwxI27N/CNR4os\nFS65RK/XHB3uUxzvUDc1F/MN337vQ5Z1x2QcWTvfB/4IVG/N/kezyS5VZpHN6gKhDGI1ZyFhdnqf\nshxQlRW37rzEzu4Bo9GIerNB2FN8KJmdLRCu5XRWofYOmBY5xTgjcxq7cqgoiNrhgqcRAZUZTJkR\nRSSIpKkQStFsVtRty7Z1WOXorOD89ILxoEJrjYsdcvuU0bAALWj582bx3pfjEFNhUBKiSHNxVeU0\ndQ0xo6m3ZGbA6ex9lkXJ8dGUzgaG1SSFbuSaoii4dvAVQudZnm65WK6ZbR9z+uQxjV2z3dQ4Z6nb\n5LIj7JKLmSGfjgg6MRulKJHBAQqhoCNcISJSSVatxcYAuuJoZ0Dwnp3BkMIEboxzzh8/RLPi5Pod\nVqstmTb82j/6p3x8/wE//Td/ipzIeG+XOAnUZ2ua+Zp6saRuLOOqoBxeQw9fpNluWOYlTxZbHs0u\n+Hi5oTKSo+mIRZijlGDHGJabBmUGHE4GLOqGdeeQKiOGrl/0ycRw64vtswy3PuM6EWx6LQUh4ITk\ncz/8F/n49T/krde+xtBknNw9pl40nLoF43LEzekRocg4Wy8wuWa2WKCNYbVe03WWXEn2phPaILjx\n3AsYKfEm770Nek6KBEI/LkrB9eGYsjTsjQ3rFp6cPmVaXEflJVuxYbFeMRxkHO/s82b7hP/mF/97\nfu7v/HtMshbk5VIuMjjaIfqkm/HOIhGYUcXu5IguWF57+z6vvvsRbz44ZbQz4sOLFVppGh9o/WUK\n19XJCc+Uh8uJIsYIISUshABuY6nrLQsluZjPKKsxt+6+RFEUdO2K9ebbxLYmxD1+/7WGk+szfuiV\n72OgJWZ/Qhw0CVnoAkJpqiIjyuSN6ayDKGm7DevlBUopVk7y4OlTqt0KnRmckDQi44WXPseDJ0+5\nfuMG89iC0owm4099OX5vFAXgCnkgtUh40FIleMpbbNOgtSF4j8r2kUTWK0s79NB2mCyjqjJwFtcJ\nbNfxdPmAbbNhs12w3i5w3rNtbUrPsY46CDazNXG3wHtD7Dwh75Aqw3uFDAIjKkRoESrNiz4EPp7P\n2R0MuXY4oSpKYiewTcu9g5yjgzGvv/sarXccXLtGsPepY8toPOX+Ow+Zz+ccHuyneHejKPcH2Lol\n6I7YKIJLXv11syIvHNv5iqcXMy5WGxSCMjfJ6z83SK3JtKAoc+puxd7wkLNqw+m6xocGgUyw2lWU\nfOSTmO3UB18JZUS6SFUSTBBFMmXdv3Gbj959H0EAoSlHJcfTQ8bVAGMUW5/8L3yIdC7SuJblakum\nFdWgJNMK6wJameRLESTBh37GFdAvkC8vwWFRkhUOqyRddFw/OmGYGapJzlDuEYstdtWwNed88ZXP\nUpYVv/vLv8yXf+Ivc3S4g/ApZVoDKIUaFYToEXlKbG7ilu3G8p2PHzNXBmVUT1rq91PWpp/H2Ssd\nyR9HtOn1vL2m4DLpoY8/DBFrLTQ1q82S1rZMipxRccJs+QFIy/nsgk3T8vK9FyjHI6o8w5icQRRY\n2/b0fId1ltD7hjf1lrrbQoS8HNLOlpyenXJcHpHlA6KzKDnGlIrV9i1mT2DCEUILWtpPfSV+zxSF\nKyWXVJjxmLheMNRgnSYEwWprybSgrVtsY2kl/OH9M0JwjHUa+ipT0m03SVLc1rTWs+ksddeydY5m\nW2NDgsnWjUVkJf/ZL/1jfvxf/0nKQuFlhY+SZrEghEie7xF1CiZNjs8JJt0f7TEcVIzUCe1qRutn\n3DnZ52/+6BeQox3ez75DtxJklWKyXyFPPTtjgRMly7NzhBQU165RliWVzonBU00q2k3k7PwJdj7j\n/tmr1K3nzvPHzGZLtgj2BobD/X202CCkZDQeUgDPyQOerk7ZGeVM82Pm6w3r1uGF6peO9O7YpOsQ\nkMheNHWZCZFOahFSNBqALIfs3ai49cqcd775Kh89OCM3GrxgsF5yvhmyrmvm23QHXy2XFFpz99oR\nx3tjiiJHCMvOje9DSoOOkk0T0EISVUIctBRX7kZaS157/Jij/SmjTlBoyc0XDlicLtmZZIyt5qsv\nvsDx3g6jyYhhYfjqlz/P7NYNfv2f/BLXjm9y7bnrTCc7HF2/QZblaFMifIf0kdBanpw1/Poffouv\nPT1jb6wpRxnrTY1WBh88g90d/GqT/C5iCtv5JJbm2b6BFAgrEmyJkFz6YIYYiMLhRcOjh++jlODi\nYML2fEa72dK1j1EIdnf3+Nob7zLShq+88gK5ySAGgkvq4M62qOh5evo45U6GSFVW7F27hY+C9dMz\n5psV3/zV/43x9IBbn3mBk9t3MdSsbjjO6jcZni7YHR6zNzr41Nfi905R6D/+GD1uuybaFkitYNfW\nSCmwbZOox96homK3yhgMd6jXZwTfMnv8hLZpuDhbYkqDDaKHydIG3LmU6psIUslQY7nd8Ju/8c+5\nc+85Htx/igw2mVfYGhsvyHcOYZssXy6Db5+uarZthxx5Jrnipf1dXnj+NlZKMAXj8YDt1qMzRVaC\nDIHJtUOefDxLjkZVxbpdg4xE7xAx4qzDek/jGtbtlvlyReMcbz+WNDEQtMZoRZVruqYDGVFaYUNg\nNBiy6lYUhaaQmrsHO7x/esGmX0z1ntPpzicudwrpc7+MPLtM7b50e5YxoqQgCsmNOy9QLze89/rX\nWQFN25GpxP5L8GNHaDsGec7ucMD+dECRJ1ORclpQHpwQmg4lMjLVE9NECtC55KBUSqEkdDHQhoBu\nHXvTKUiBwzB/Gnnx3pRCBGRr0cIgo0B6y9HRPn/tZ/4W//R//x/o5u+yeu4VlM6oqpJqOCI4x2Zb\ns65bfuVrr/Krr71FdbRPnC9osEQhcSEZsMzOzp+htH/3GZrySMTV/HAJp4aeYxMvI+5FGjZ8DInL\nEgOLxRPC/0Pdm8datuX1fZ817OlMd6x7a35Vb2xeN91092swpkE0jY0AExTbJB2QMQ6eBI6lxIki\nZ1DyR5BiESdW5MiJI2wL24KgIAOGdjCmCU1DM3S/Hug3DzXXrbrjGfe0pvyx9jn31us2/XBs6bGl\nW+fWPfucs8/aa/3Wb/j+vl9j8dZi24oQPNZUfO0zT3F0fMTNuzcp8hS8wJkW532H8IX5fI53no2N\nbfqDYezkNAaF4MHdPcpygneCzd0dtkfbFIUmt5u4xKJ1wTAfxrDwbR7vCKMgiI07SEW1mNMsAgmW\nkKfcnR3HTkYfqbysabsed8/d/SMubI04pxWTymHwMQs96FEbR9PGmNq5SDaCUKc97iqWzIpsyGR2\nxK03U7LhgMVsitIaUykGA4+udeQQJHIxBh/4nvdf511Xdrn+2BWEgLzQmHqGy3qoImN3eIWpOGGU\naGayR9I3rO0kZMMhxlm08GwO17EmMD8Z4xLFtKyZTA55cHhMEILttYIHD47YvzMBFSf/5s4mmxt9\nDg9rytKgtUIJDUJwaecaN+7e59z2Nt/wzGO868o2J7Om4yoQqNA1c3UDrqRa9ULEPE4XQXdc4M45\nFB5nGs7t7nL+276d6ckBD/fuMZnNwDlSAVmWcG1nk41Rj9HaOjpRFGmLTO+j1i6y9rV/HpX2mBxP\nEKLFE3sMTONQwLmhJvcSUXs8ns3NXUZbfXpJRrImeP3WEUO5gOHj9M9dJK2n9NMMLWX3E7tY1zc3\n+dM/+FewTdQNffNLn2My3+NgbJlXns+9eYfXTkperRNkmnNBjqmVwhkRtUUIHZQ9xQcD+M5bCKel\nh87NEl252ndisL7zdILz4Gx3/qMwyFVo3GFxtErI84I8hWeevE6oF5SLObZpOZlNaJqG/igaxYtX\nrqOUitIAaU7bNkyqI4RWrG2uMy1nGDfn5kvPc+ncDhevPsHG5i4+CUxmx9wY73Ftd9me9NWPd4RR\ngCUZpouNUSGQK8mGlrxhHJ6IqrNti2lbpAhIqQBJ0zoGwx5VWeLTDNtYtLRoLWiNQ8oujg7ERdDR\nbaUhxSDizZFrGN8SFmWsk/suQVWk2KaJ8TRRzl0Af+L9T9Avesi43hBEmDRKoKQn649w1RydF5Sz\npqPmEuR5ggxRxCVSwivU5pAQBFJLjEm5fH4H01bcOHiAC7AYn3D56i6Hd/dJlMBbQ2uqrkU31p+N\nD+R5RpYqqrJiZ7ROmgwY5rH1GwQJkQiUDiKuVSeLRpy3smuOirKZgRA6+XrhSROF0AnPPPUMCsHB\ng5t4E9haH7I+HLI2zEkSjdIKbIsYDim2n6I4/x6czLBd/4C1DUZprIuAtEGqGKQO4QXKqq7FWyO0\nRPZ6WFuR6Zwk38EHxf2DMfM33uDbvuUDaCKGRXb3FKFQqYidoVrxzHMfopqNebD/gONZyVSliAdH\nHNyb4QK4xmKFI0hWCVjp6VrLwxnMR/Quv4zyVtAR1ywp28QZO+DPGJKwSucsa5uxYS1g2hqhND6A\nloJUS0TQrG9tg5Cx7VtKvIpha54XKJ1QWxvJWbXuyIgCwTlq7/jsZz/Fnbs3+aZv/R6SLGVttM5g\nuI4Wf8RUp0OI6kSRT9+SKs8Twz53JmOMc1hraesF2BbpLUonUSlKaaqyJbm4w7Aw1HNDrhJ8T0Fr\naZsoOe8lJEohlSTNc9Isx4mUed1wMp2QaI3xjmk14dxoA+kdWS9OUmctOisiZ2SARAmyfobMsig2\n4yymbQloXFOhFjlbcgeX9dl7eIzSDidrEpUQWstg1KdezAhrffAWpXNE1seMF5i0pZ/0qBeWtrV4\n0+KdoQiO3fP9qEQt4q4vtGQ9TRgOM249HNMG2Fpbj5UIJVgvCtrUU+RFlGJ3scQbGYujYRCic31D\nBIKFICP7b3BAwKGRxXrszBvvI5znyavXOBc8eaEZDBOklGSpRgB6sMPowjMUV55B5gUOR7l/l1T3\nMCgWjce4FpC44ChDQ1srVJIQpCDBkycpyqQI6ylbAXikqyGk/O6XbnGlt0YmIUkLlOqk3qUGRMe2\nnKBTTY+ELMnp9YdcsS1aJ2xdusIn9z7NeDIj2ToHwZKIKNIZ+xYEzsYKmJQyyq75eA2h2xCEOF38\n3ndVlGXDzjJpE2f1WzBP8UU++IjM9Y6ma2Kz1mArg3USqXr0M02SpiR5L6pXZylCSvJU4lwgQSDK\njFRLVJrHcUgkaS/FF4Gb92/w2v/+P6BUQm8wpOj32dy+9LbX4zvCKEDM2voQCSn6vT4nbcNRN9g+\nECYUZbYAACAASURBVJFqTiESuqSYBBFfE6TGeRdvbIixapYGskLjrScE3e2MivWNLbKix6y1tCEy\nD1lrkdZjakfv4gBblbQ+uqdSCryIwuGSbgeRkd7slKe/s9ZakeuUQX+ANQ5TlmAlOlnDyJK0yFFI\nVBAIkRBCGwn5E4dLKpSUJD6hnnsWVUljLATP8fGCwTAhSRTOB6SOPf65Vmgt4g7vHHmWUreG1jmU\nV1i6BKmKrEvLZNgyhwBRMhHR+UECnAwrEhFnHa5qmBxNmRw+xKNJEsVgvYeSYSW3HqzDqZzhpa8l\nOXeZRqWoAHhorEApSWsDJojInO2jBxecZ980FEVOUiQdZb2KuyeeRCUY22ICaAep1sxDi9JpJGaV\nqkv4RRh6fARkpJ+XQaNDjg9Qzydc21zn+7/+aX7xMy8yI35PDWfQs4pIYRlQoUPYLido53DGtX5a\nllj27Jw6EstwQZz5H6e5iNBVLYhiRT4ElIpCx4SASgRZGr0ArboWap3E/ycCqQJ5knJubYs3Xn6J\n6eEBzkTOBqscITSIJIs9K6bFnBwznYyZHJ287bX4jjAKAbBIjDcURcqlQvLKuKE0HmNarDUo4UnS\nFGcNtkOb9Xs5Vy9f5NzlC5TTE/I8Q7uWQS8FmbM+yOKCdVHMdbC2xmgzynvvHx8gD46YzefUUjK3\njvV+jyd3L/PKvRskjWBNaZ4uclSquCsUYxNLeDYQdxRvIhDKB5ySDJOMzaLH3Yc3kHWLDZ5EZ9Fl\nTw1tcOxe2CQvclRvDdwQU84IjcG3gsQPcd5zeHTMZDInBI+iop5XFMk2eU9SlTW6yJDYCLBpW5yQ\nBClRxEepIoekUh1+38UcgvBxoocQoj4GHVZAnCER8dHYWeDe8ZTJtGJ+fMDO+ohMrUOACxcuI1zF\nYv8OxhiSreskm+dxm5cYOygPT7BtizOGXqI5aRrqNhrQyhggoEVkLRbBUrYWMQOlklXrsXMC2zV5\nHUxLOGx437t3KaVjFhSjRONVgtQJXijE8rsED84jVIJM8tj67T2PXb+E8Yrv+WPr7Kz3+fu//Blk\nb4jv1MlFZ4RyoXDO0zQ1TV2D7yDLwDKEBB+ZvB7JN4RH5nP8pYPFLxOU3XNRNkTSWseto5Ln3ned\n+eEhENmTEp0gpELrBK1VZNbudD2kjCESwfKFz3+Jsm5BxjGzrcS3HkRFNPx6lcto2vptr8d3hFEg\ngJcCLyBVCWPvqJzHd6QVSkuki1DjgFxpqfRTydagTyIUWZZT5YpUaGg7VznLIqIwy9m59DhCq7gT\nicAkTcnyDJ3lpKIlVYb1fs60nmOqBq0KBkKQiwSX55HIIni87XZSKTDGYH3AC4WzgY3zF5jOZrS1\niSUuR2yZ9gGpE4qsj9JZ1MYMEdfuowoL0ioGwzUWx/vU8xLwpKlmK1vnxs17iOEAY3KkEKROko1G\nDAvBUbXUh4zzLk8zkjRBJxrfRtdWpUuZuehZCUB0QrrB+y4vEg3FrGqYlAsOx1Ne2zvGeNjtKVCC\nRBL5JMo5iZbIYhOVw7TYpK4dyfExQgqCCyg6sGRrWQrUeN+xR/tA611MzAUPLtB4T6JCNHQBpPR4\nE6nipJT0N1KGl57h8O6XuD9Z0B8MYuchHtGBy3yHvYgDEn8XIrZNa5lHzQoUX/PEVQr5e9SmAa2Q\ngwyZeHQpKNIcAD/o4a1jNl/Q1M2ZKgPQcW0sq7zdFOasYRBdUvKtvRPLxO7yT7dff50Pv/9rSVKN\ntQGkjNheISP2Xy3vbxSmsTZqQbTG8+DhHtaaFaUeqPhaKRDSnIGlnAofvp3jHWEUAtAYCCHBqYQ7\nsxmVl+A8LrL4E5TCORNdYSzBWs7tbLG+OaJf5Az6Q6aHh5F6bRAITpGnI0QaZb0Gu7t45+i7mgTP\ngzQjTXv08yFatggU165fjPz5sofspaznGYmKE6B2gcZExF9rDMErrLN4BzqNWAMlA/emY4QXIGO1\nXyQafNRD7A9GUbfRO4RpAYWrFpFnr8gQiWbmoohtmmjKqgIh2R4kzMb7DNY3cASSLKPQOcJqpuU8\nLkQfOmMQu/yWvQU+OJwRXeff0vU9g8/rNAYdUZrsi2/c4Xg8w1qBDdH1dw6ME1TO4KyhnE9QWmOD\nAJXH0llwnOs0GKahpbKOQifULnJQeFPTc57GQwNY78CHGEYAVevwwiF1B8rqJ9hJQyIlx9Njdja2\nqKd7BGd4/d4+FzZGpKMhwVmkUqvFJ6QE0X3/TrBWSolooVjrYYC13HN+rc9r45p+npFlQ0I9Q8lA\n2m0cRa/HoN9jXi64feMOZdVQtyaGs4BahhDd4lapjmSvbqm3EbqE7ekch9OwYln6ffW1F9iffGsM\n9WTAiEAqY7jnRTTUQXWUAQSCElRNwwsvvtzlJARiaVxxBOeI1ccOGNZdZ/hD8CmIPwz187+r4+rV\nq+E//y/+syjCQTfOSzN85vIiTCCcDnp3yC4mFPLUCq+YjJdlN9Eth5WR7tiOVZxAkRZ92ZW5zNTH\nONCH6CqKEHA4fuBj3x8t+ar09DbG8A885Ss9GbqxWI1I/Fv08d/yyuXz3YIPnaCIkHzo2nm8C4ii\nm7RtLAn67sVS0FHZxD+svI7ub6eEN/Hc5fPLqwpE3IcPrJSNlyPioOtmjWxtkphlV7BKcNpu4fhu\n7P+Pn/+VLvEpzpTxWN2j+P/uQrq5u4zrfZeDOrsQqqZm78GCQT9la5RHo+/dCtgZOqZmRLzPx2XL\nuSIiO8bjAw6PD7m8ex2HYF5OOTk64M7N17Gt4T/8oR8h+Kh/GjEFXXUhRBIWH0AofXrNIaw8tpUE\nYViO2DJpKmMVJ0CaRNHdZXIzLKkFOoNDVylalkZDiOjQGNXEm2HaBmtbjh/c4ns+9pc+G0J47g+a\nifAO8hQguvUr0ypOa+rxpLCabOEtr15Z4c4anBqH0x9xdoYtj64xaLUMRFwe8ZTOQyEQ2Z4CHrdK\nzMVP9ryVO/st6aW3fMdHz/ryZ8Tq36U7+ohb2sX/p88sF4VYfa8IzRW0bYlOCjIVMD5gqojOU4SV\nMMhSKWo5ztG5PmMUVsN3uuPF2l08z8fEPQFIFLhuAtvVXA8rg7Ec9k424cy1d4tKgAqdXyiX474c\nklNj95XH+ez1LUHH0Ug0bYM9MaT9CJH3xNKckN0CCxEDo1X8Ihu5xPpAIgVaaXpZgZPR49J5gsUy\nLBSVXuYwTkFNZy4VS+QdVd6vVMofyTp0FZ6VUThT1YiMK4G0GFDNxlE9jBCb3Jbz4PRhNSKn3uEZ\ntijieC+qird7vCOMgmBJw3bambb8UssdZXksvYXl0hDQoQhOJ40InRfQTe+4vJezMqwMxXLA5Go3\n7OLAs5/Fqcu33E/j/zxR6285489e55cbBbFaeWfdn7NdeEtX70yUuvw+j5iIs75B502IM69ajleI\nNX9C5GzVHYGnhNhxF5aXE1afKgCtBd6FZVSxgngsH1MtsC5QpIK6DWwPFEczRzZIaCYGJWIzke8u\n0sku2bk0Omc+DxG9iWU+QAgRKeVZ0qh347K02UsvRpwd4/h8ENHghNU4xji6l/XY3gpoIMsyZnUV\nsRsiysy3TRQJqmxDWdUkQTA9fsCVa4+j9ACUA2dQOqFdWKyV7JcKTUD4M3dt6QGIaKTbefTWZF+u\nrKtcuvHL/JTvFLGDJ6gUYzzWegZ5D61genQYeR06zITo+lO+bG51lbgoqBQ6/Opy/gjaxjI++SNW\nfVjNGBHvbOf8wXKvXm0zYXX6ylp2/8Z1IFZr86xHcdZCLzf6OCmXkF/RsR2f7rgr5+HsNYaly/qW\nn0cYesJX/PW0jr186uxrurd55AVnPIG3nnum5XllYsSjYyF0fCaXYMXpjqG6x0dCNc4YFh8rA+LM\nE6tvGogyZAjmTdwBH0wshRKUU4M69egRneZA0j0qGe9lLjvmTbH8fLFykcXKai1DhMDScxOdB3H2\ndiz9qtX3OGtviQzLOpHoixLbWjyCPMtjR6iInH7Dooit4iKLvQfe0es/RkBEIpREUdUlaa8PxHxC\noKNaE50XJB9x4uIdSwymlSwmDcN1TSZ1d3lxHIyLzU6FSpBS8/B4ys/88m/w5q1bfODZCzz3vg+y\nu7ZBlmnW1tc7zc3uPTqqwi8/TnU9lhDsVYu6/XfP0fhv/zgbH4bTXX8VQ73l1BX3ApGRSSytgRAR\nrgqETvRvyV+42tWlXN1csYrZlrP5NNSINyA+F91c99blyamJeeszZ3zm5WxdupEdAs17y/ToIdaU\nYCfYtqJeTLsdR6B1hkoSivXzpMU6eX8TIaNAarxOv3r30xHpxkIkuODJI4gRS7Rdy4nswhmnqXv+\ny79BfLLThKWx8f0rF5BKYFxgLdccNREGnomAI9b+XbdIklThraOXCHIl2B0qMgG2iQmx4zZgQ8SC\n6C6LLjqMwFkv6Gy/xllDtrxOgSB0i9MTOvGfqD1h2pobn/scw80RezdvstibY9MSEySth95gyPXH\nn2a4uc369jaJVCjAmYa1Xg4+YTyZReFgHNJ7/GpOdp6bjMJCWsTxH/YKXAHzVhKCwBqLlBLnHS+9\ndpPUzbl48QK///Ahn/md3+YXf/bjUevTe77wG46f8D8RPQkhkFoxXFvjY9/3p/mGDzzH008+TiI1\nKs3OWPPlOuiqMJ0XiHU4Zwl/iKX+jjEKZ0N9lpsEX77LnzUSj07iaAzO9vwtk2erLHAIMd/Q1Z19\nEISOZff0PZdhhDz93DNu/OncXIKel+HFsg35jG+xmrSnVxucxbmW8b0XOX7wBje+8FuEusTPpwhn\naecV3jpMa6LhkgIxWmewvcOH/r0fYLT9ONngXHQZUZ3XcPph3ZUgpY5kIaLbfCWnXdNnt9TuGvVy\nqFia2tPkYePABiidoHSxJFu1AS1hMY/LQwkReQp9oFAihiUSktqRqrjDCgE6QJ5ExW5jA/Pg0f40\nBDybnV8+Lr2A01Leo76TPDN5ggDhPcJWmHLG+M7LnBzsM73xMtVdRT2eIaxlcdDQ2JZx2dB6eOOF\nzzFY3+QbPvxRzl+5Qm/Qo2omrI3OYZCkraWaVBjv0HlGW1cs9TmXn66lQHQEw9Y7BArrDJOJpzeQ\npMohqoa79x/wyf/nE7zn2Ut8/Dc+RX+9z7wsV2rWcc6deo7WGsbG8pP/5Kf4V5/4JH/tR3+UJx+7\nzM7WVodJiffTr9ZHN3bde3nnYhL2bR7vGKMAdBP8dDs4NQKnsRsidDmC078KEfAiRDZdwPmW4C0H\nD+6z1ksIxiFdRdNUNNWcyXTM7vmLZP0Re9MFxXCDqxcvk2Y9jEwIgbioQogO2dIQuba7AR0923Jx\nnXX9V9v1Ml8Qz/O2oRzf52f/zn/Lw9v3uDho6GlJUjWY1jGdthgbKBvL5iBj91zB/Lji6LhkqO9x\nq/19fu/nfg2yjO/9kR/m6Q//Kfqb1xDLBO3KfHboPKWi2nRnFHzXohzZo07Dh6VTuUxFLL0I46Fy\ngsZD6cGFmMRSSUHrAsX6Dj5YpKtwXkb+QdeisiFlM0brc7j2gDwbUttjHIE2CIaNx7jA1UzipaAp\n4m5dto5kucA6SyDOLP4lMcxq+Z81cKLr6bA1vpww3bvB3c9/GiFa2vFDpBDsjnpUVYMY5swmY6Rv\nItBNWEpjqRYzTo4e8s9efxWf5IxnR/yX/83fJNVbFMWAXj/H24b9g4cIIVf0755AaxtSnbGoG3pd\n5aCpFkiVMhuXFElC5jwqCIxr2B0qPvX8p/i1T7dkie5g6C7ewVWOqjOC3Y1x3jI7HvPqyYT/9D/5\n62R5zvd935/hu777u7h69QoydMpUXfgd+4jABUdVztk/nr7tZfjOMgorL+Dsrsfqd8QyvAgdS1Pc\njSQgnMPUx9h6gTIV8/ER7dGENydjXn/zJlcu7dCUTaeo4xnfvEVS5Fy9fpm1zHJyY8xkfszG2kWk\nzsi3LxG65GMQ4JwlDQEn1COXvCqBdoF9dBbUI9+D4Dm68Vm+9Cv/FPvwTa6vgStbpjOLq2ta43C1\nxbTQmsD9umU2LVHWY2xgUUcdyw3lqBvHr/7kP+H4zut85K/+LZTOOc1XnP3MszvsKlRfmbOYcDwd\n9qVJcUHQ+ID1gipA66NHtTwv9kV4mnIfMKhkhMABrvtAh1SRD1DoTRwZKsypbROJVUIEJz1sHCII\nys46yc4YLa9ILD2vs4bgyyLpLh7qmrkWR7c5vvkykwe3MfUxSgnGkzHD4Rq2avBB4AqQjcZpSSIT\njAso7QktsfMTj3SBZNDni5/6dT7w4W9n60JK1svJdUKi1CrkDCEqYAkRSVm0CChJZFG2nrqpSQNR\nkKgLS19+/Qb//F/8UgxxQ6B13TxeflchV/oRK7LWM2vDh0CwFl+W/MLP/QK37+zx537kB7jY22Fj\nfW0la7g0oME7ZrMpi8UfQTo2uUxohTOuYZd8OnXvAzhPfXxCcDPs7B5SNZjxAdXJMe7ggJ7O2Tto\nqa3nziJy3gch+Oz+jZV7LAUEERilmruv32XUS7l8bZfhqE9b3UPnI6YPX0KmI4JMqDu9AETChHS1\nCE+n69l499RDCN7SLI659Zmf44s/9w8w0zlDZZgfCurakKSKa5clqhH4yuMtvHyj5njumKgkKi7R\ndeMKWMs1PRlwkyN++599HGVLPvAf/Ahrlz4YcROdEy4R0XMK7cqDeSQ866o3PnBaPuxKiQsvaJ2g\nDYLaRa5CJWKy0HtPaxqK4RYew2KyQDRHKClwISbflA943wIlSo0IISPIbVrzkFlwPNCCbQc7qcAQ\nsDHtE/sbutr7ac5gOcJnds4z30MICcJDO2f+4E3ufeFXyXUgsyUHkzH390+4sLVGAPJcoIocXfSp\ni5RkuEM13mfRnkDbkipBaz1SQVkvUELyxc9+ntdeeJnv/v4fYvexa4wGPS7tbLGYzQmjDNfGRrf1\nQZ+yNpyUc5qFpZkZellOv5ewPsqxLrKCK63423/n73J4+BBCQCUpSoCQalWKlVqT5QKEo5o2sXdn\nWZ4PokOlxnsxHo/5jf/3E1hj+Y//8p9n0C9IdbJaK1IKzOyEBy/t82DvbcmvAO8UowAxsxxYAVS6\nmRpD+xBLOcF3Ggy6IVGBsHhAO59Q3n+Ad4F5CcfeMK4cpfVM2lNSFesDQyU4Nu607OkstfHUjUOn\nh5iyZPvCRvQKBj28a7HW4UyLsBYhGoTPllfM6c78ll26+wLN9CGvfvpn+OKv/Dzt/YcgBdtXCyal\n5+DBnF6RYkNKgaVvBSqxFDuavTbl+X3LwawiINnMUwQwrwyDVHFtmDJpPV/49c+Q5P+ID//V94FI\nTz9/VRd3HTDoTGMPp/vt8lEKqIPAIFBC4gg4YnkSoRDeUbdu9fZKhojk1B1KUgjwHp1kFGtbtNMJ\n1hp8mBNkHy8T8ENcmDI2HgUUSmBFlwANRF7Es0mYR3JMp8g8wtJYdNUIH1gc3Ob49iv0sziHTAi8\n+MabyLTPLg6tBZfzPvvOEJwkK1J6GETI2e1d4OjmXcyiQhKovaTY1CxKj2gajPU8/5u/yjcXf5Le\n2nn6aUa/V2CcoJ5V9NYEtYniLHma0IRAPlD0+sNu/iqU8CihqOqak5MTBJIgQAsXk6siIe9tgLBI\n5UjzALS4ytK6M2VIGQOqZd9F6IzD7z//GX7hZ7f4iz/0MTY3d1iyPwkBZVUSiobhKkf21Y93hFGI\nmXa9soIxJur2264TMQSJ9w3Kl9R3Pk1zdEhzeELZSBYLmJmAdVBbw42FoWotiZLgY00+BDjxUADG\ne3wA4xzBeprG4p1jerLAW0cx6JFZC0rjgkD6FmsDtZug5YWzV949ni6zWBZ1mPKQX/lff5jxzfu4\neRn5DDyc3ChZyzR+M8e0AXNcoW3gCMgJ3CSgnMNbwYX1AcO+Yly3LIxnlCVM2orXZoHRQMBswif/\n71/miW/6RXae/Q5U0mPFYNVdi0wFdRNNQgQSxfFwseWA1oPxirzYBq9YmEOmbUBLSVHEDsOWQK8Q\nqLSPkAHvaqRt6acpEGnUhIi8DnZxSDrcINM9ApJmfIggxYUa5wXWCBbGcy1VDLTg2DkmrYuJX7cc\nyvBINeg0rOTUkxCgXUU7ucf05V9lqAQn1ZyjScWD/RMeu3SZVAqm+4ckbY90Y42hyjhezKjaFt80\nyKbBjEvWhpLa90jHDX7R0EwkqZbM5zWJr/jS8y9w87U7/MBf/EGyYsi5YZ904chyhUfiTRTBSZIc\nLyQLE2gUpEDwDc5alJK8/voNpBBkqWIzydjo52xtnWPQG9A2FpX3MGaOq6b0hzlPfuQKtpbUk5oi\n61HkGSUV//jjn2BRtzTGYq1jPJ/xS7/489y5cZv/+cd/rGu2qnFtzeeff55yeszf+Fv/NX/vZ3/6\nba3Hd4RRgI7QQoSlGswj1bxO/DsuzvKYcu82buGZlZa5kezVjtI42o5ANNOSXKdsKMFGqpgax+15\nZGySwEDELLolrKC5i9bhCazPq0h9Xiz1B1OCMwTXYs0JQWw8ct3xCGf+BdssOLzxW4xv32N+vMAb\nRy8JCBeoGotLNNNJw2xhMGnGy43nyHpaD2BIZYqTglsnDYNa8uRjKYVxNGNPlihM8FStoq8DEscr\nn/w4xYWnWN95T7d+OlZsoajqcJpU7BKIrvsBqBwIleCspLZTZm2gSBSJllEnEUkvz5FJTlVOCMag\niMQrvUyTaskgi8ZhfzJj2rRQnWDCBJ3l5L0cbwO+jcZqbiVBwu+Xjs0sAnJs582JsxnyRzwFVqFf\njP+AYKkne8zuv4T0DdY4prOaG2+8yPHBnI2NLeZVzdqoIO+tUZeGtZGmHQwoD47wPpAmKb1ey7xu\nUAPwlUDMPcI6nJG4EGKHpDUcnJxw+83X2b18nV6W4W1CmmexPyRLoI2woUFvgK0rJuUeQlhkExAh\nZWOwyd07t8mThCe2+nz/d34bo7UB/dE6TVtjbYJIM6TUCBxeevoptG3LfNYQQojco2HAtz73Xn7r\n87/P0cxHZjHvaa3jzp2bOBO7Jr01tHVDVda0lUXk7dtejW9HNu4fAH8K2A8hvKf7238P/CXgoDvt\nvwohfLx77m8CP0wEq/31EMIvf/XPoGMIisw/IYBcukddmc+HQHt8kwef+w3mBy3Uhoczx7ExHDaG\nTMIHr2yyNcy5sLvF2sY65y5exlhH21ju3bzFzTv3+NyNfSaNQwk4nyTMG0PlAz0b2ErhYG+GWjRc\n02DTjLSfYpFMHYzkFsG3rEKGFb7hjHEIns//8x/nc7/ycey9Q6bzFoekVJHeLNjARCjo9UmLIfvz\nEpsEQprig8ZryaJu6SlBPV1w59iwP8u5spaxPYDGRy5D76ERns3EceM3n0f2foJv/gs/HlmIOrEX\nJQdUJuBCWFUUarfsUoyubVpsgCuZ1/sYLxhmOmrjytidKpIM25aEek5PgEoll7Y3ubizzde/7+sY\nZCm+bZlWC27t3WZ/MuHVW/eZ156FM+hCYoJAcQ6BI6sPCT5QucC49RQyGmbjAs0qDFuO6FJxqfMa\ngwAp8KbCLY64/2v/F6PdIcZKbr3+Aq/dOmZnd5vHL7+Lc9sXkEqhksi0VQfHSVNTzeakXnAyqwkE\ntnsZs+OKcm8RNULXJaYJ2DpiHcoQUFWDVIpP/ItP8J73PcPXfOAbOxSmQEpP0xgkgbatuHn3DWrn\nSPM1kgQqGZgfHPAMBS+8/Dp/4Rvfy7e8/0mEbVHNmMG0QSeaYrCGTj0qE5AUBDzVvMJKSZ0InA3U\nw4zSKq59+4f5vu/6CP/j//YP2RuXHM0XeB+YzifM5w06dbH1u6ppbUPTVoR5+dWtQXe8HU/hHwF/\nF/jJt/z9fwkh/E+PLm7xLPAx4N3AReBfCSGeDuGrs0ZGgpRYWgqig8Z2MZN3Ae8di3s3uffKTXTR\nI2sDD1uH83Cxl/Ds5S2+4yMfZH17i7WdHfJeQV70wWraxYJrVx7jsZsv4c3v8saDKbenLfutZYM4\nDecuoBqPVYLbM4M8aVjrB/oQSUh9oA2BEL4yrdUygx9szcu//Wma4znTeUvZWHQi6EuD95Kdx86R\nDHdJexsYE5hNpjR1Q1019Hs577pynuNqwauv3WVzmLNWGt68eUjfW65ujkikoDIGoQXGWZogEXXL\n0SuvxaYc1BljFWJrN6fegQ0SLzSoIUoXCDmCsMe8rdjIVLwFHdhL4MFbjHHkqSZXmlxL3v34Yzx+\n9THedfk8MngWszFF6nHNiH6qufXgAUHluKrBqeglWNeQ5ENEmaBVLPMa1xl/DyaI2Dz1lmTiI8ey\nymJK3OQBg3UFNvDwaMzNh0dcv/4El85fZHtrl9H6Fj4EjGs7rgiLdS2TI2gWLaFtmU9L7h8swAW0\nkFFKYAohDXFldKGssR7lYTxbcPvmPa4/W+K0pg6Wo8WUNTnAVobpyUNKFNYKnr16DRkcs6rBXzzP\nzTfucrB/yDdfG9EuShKlODqyZJccuepFLgk0EokUCh8kwhqCbTDNjNa0zKuGxjn8YkylM977Tc8x\nupPy/O/9EsZFncmmtQQC1kbq/UCgbQ1uNvlqS3B1vB0tyU8KIa69zff7XuCnQwgNcEMI8Trw9cCn\nv9oLl5lk2WHWQ5dLUMGDdARfsX//Nlkvo2o8xnkq4/HO8rE//m7e/9z7uPrUEySJJslzVJJAsIRg\nEJljmDdcvXKJr//A0wxevsH8lQPGtUV4QSZg7AMPG0eaKHI8D09qysax7gI7awKtBNN2FfT+6waL\ntjxhcmefet5yUkVGHE3ApJKLl3a5/oFvJl87R9bfxDtPNZvS1jV1VZFqzUhn5KFE9wdU8zk7kynV\ndMq0cjQLGK2DcZKyiRyPWkITLJPDgxjrS72KvQSxKuBC9A5cEHgvULpAiUiY0rT7eLdgmAqEEl0l\nqAMECRAikCeaXCu2ejmbwx4f+tp3sbOzS55EN7UY9JBa0KxvoHTCua0dvvDGGwx7G0zahnZS8RfH\nhQAAIABJREFUI6XCtRpdrBPafZwQ2K58Zpwn6eWo0BGSrHpbIqJvBR7r8Bhm9oDF3msIlZJZz9HD\nB5TjBdf/2BNcf+JJBus7IATOWZqmxDhD05QIK8jyEc7O2d4e4VrPw8MxroNseucji1ErkDpK0VtE\nbFkOAYzh5GRMU5WoXo8XXtnjtcPbvDsbkKUZvWyANgt2ts6TiUDrA1lHkDtYKyibijzfxLc1QSes\nDxyj/pA0TUmyNCYl0wyUAmuRiUYHQ9uecHQyo7KBIusBnmAs9mRGT63HbkphYy7OOazxOBsNrxKC\nIte8/tmbb3MJ///LKfw1IcQPAp8B/kYI4QS4BPz2mXPudn/7Aw/Bsq1fgIq1fieg8TW3P/881cOb\nHB89oFeekKDYqw0T49jJJR966irf8Z0fpd8f0i8GSJUgZIJIegQ8XhrwgsHaJole8OT1a2ytDQnh\nC7xyZ8xLBxXWB96TSIyE14zDeE/jA73SMC5bxouarV5GOiwINpy5algmFwPg2pLXP/XTTI+mnNSW\n0jhy4Nx2n2/4yLcz2NhlbfcSSZqTZH0EEs6n8TZYE9WLzQLvLBvnLtPUC5pmztPPPsXBvfv8/C98\nhsvnBzyzm9IYR1k5vjSb8/6nBpSzBYuDlxmd/zqkPq2QmFNdUiCghMP7kiD3MK4mk55UxsqDJ6DP\ngEOSXvy+BVCkCX/2T34Tj126zOUrj4EIUYI9BEzT0JYLhJLkacJ7HrvKKC948eY9di5scWAOaaoa\nzAGoHlmxg3YOHDg8MpHIEfQuLj0c35EWLVuNI69hCB5ha6p7n4PyhNwuGJctr79yg7S3yQf/+EfI\nBmug8qgYbltkNUdV8yjxbiRZLmimgsNjg0xSNkYDDo5ntB6EUJjG0DqPXeZfQlfBkg6MZzadc//m\na2xfe4Ln3r/Juy+NyJMEnfS4++Zr3H3+FZ7+7ou0dY2XkmANAUneVISy5vC2Y20nZXg+pUgTeqMh\nOslR+QCZFMi8D0qjnCPdvEB7/JBEaXa2SqazI6qm5c1b9ziel+xajTELBr0+1GUMyxZTlFaU9Rxs\nbPYSvR73X/38217Yfwgt2keOvwc8AXwdsAf87beslLPHV9xahRB/WQjxGSHEZ2az+apD8rRXHnQQ\nMHa42SFuNib3HoXA+EBrPRfXejz79LXIoJQmXckGhNaIELUapFTILEelGSpNyPKM0dqQ64/t8NjO\nkKbDlx/5wCzAdk+hBTgXqIyjrh1t4zlcGGaloW5PuwTeCqox1YS7r7xI3n1j3wTWeykXrjxGb7RD\n1htGT8hH0VHnLEgZm5e0ICRAGgiJQyaKJE1Jsx5FPmD34kWyvuJwsmA6c+xuaJRKmE4N8zZQpJrp\n/VfxrvmyK4v4j9P0hxCe4KrODESMwOr8cIputFVDsAYtYW2Q8dTj19k5dw6dJpEwNM0QOgElI7V+\nltLrF6wVmu31Pr1UYxc1w/VeZNFGIKhxbo4JC0yY0LoprauiqtfiDDAssCq7xesKMeFbneCmxyjb\n4q1hOplRNpJ+MSQbbSKSPkKlq/pLzLJKcJLgoJ0tNS4dbdNy7vwagyLB2gYvoldlXcC5zlvtkp/L\nHGhjHePjE8r5nEwKRnnCzrl1hA+kiWKxqLHTOQSH8J6AQ8hAU83pSZBzD0KitSYt8g6C7BBKI7MM\nkSWo4QiR97B1ixeasrLMS4s3AuUNg1ywOUiQwWJNGwF5IvJSNnWFMy3BNgRn0ErhguCNP0SX5L+R\nUQghPAwhuBCZHP5PYogA0TO4cubUy8D9f817/P0QwnMhhOdGoyFKRf5+JWXEkGvFupY8++yQS7sD\nLowS5taxMI6ydUjj+Og3voennrmO1h3TjoPgAsFLQlDxprqAtz7WzYEkS+kPhly/9hjv/bonuDrK\n8AJu2sDN2tFWgScyReY8Tes4KVum05rxtGI2b2gau8oxvhWXMNl7hTsvvcL9yjIpLUVecP7qNS4/\n/jSjjRFZmoBrCW2JbypCU3e/L4hqRA5vI6sU3iIVpGlC0V8jH2zy0Y9+kEILXplYXr7bovE8dnWT\nV+8eM503vPmZT7E4ev3MFYVHAHGC2LossSjhI08iS0+tA0p1lOUyixJ8qTNc2t7gez/yjWyub5Bm\nKUiJShQ60Uilu5hdIFVCkqRsDPqMipSL59Y4OZhRzVr6a0NGG5uIAN4ukMLjvaU1NU0zYX5yxMOX\n78arXnoInUnzXcLZzA45uvFFTk5e42DvFSZ1w97DMTLJ+chHPoyQSfSKQgvO4Jqmc6Nj2ORtwDam\nk62TZFrxcG/CYLMgz4vo82mJ0LHyYKzH2hBFhIzHukDdOm7euMODN19FKM28nPLqK2/iZeD49j5H\n8z2+9OlXO/0Mh/COVEsevnYLOa+5+p4EvR4p812Axjla72kh0t8nQ1pTYW1sMpNZwcYTl9l+9wUu\nfmiDK+/bxgVHXc3wXdOXDZHYokgTJpMjWlOjszRyVOiENElp2refaPw3MgpCiLPF+n8f+FL3+y8A\nHxNCZEKI68BTwO++rQtZSZiBFpJ+8GTWsH/rZRbHhySNoRBdjiF4pAhsbsd4Cqkjg69OEDoFlcT2\nUikJnS7iUtlY6xStEwSaPC1Y78Vy2tQHZh4eGMfrc8tIQAZkgZi/aAwniwWT9i05U3H6sDi8QzVb\nEIDZpCLXCcVwRJJnHXRVdskriZJq1V8RfNwtvFvqXnRd8aFTbyLmXEa9nK95/xPsH5WMK0dRCOrW\n0ssSbu4dUI6n1NP9MyX9jleiy90tEcEiRPq1IBRSp4+oIYkuhm+rBi0EiVJ88N3PcGFnhxAsIbj4\nhkpEjkQVYdAuLFmuFEoqMqUZ9nrgLIt5yfxkStvGHXHFQtRRp0fWq25XDmeMwuonegquKTHVDBfq\nWIWwlum8Zn1tje1LF6JvEGz0KDh9HSEyEkX251N+xWWTXDlrKQpFkqpTYttunAgB57sfFwF0dVUz\nOzxCC0GW95FKkSqNzgWiENy6cZcky1EEEJGqrp22tNag8yzKyYsIDrfOY6zDOoeVfbx3OFHgswSv\nFd6Bq8EvArZMMUKR9AZ4Ehbe44HautiXkq4zOTnqKjUeSVS9Ujple/vfohiMEOKngG8FtoUQd4H/\nDvhWIcTXEaffTeCvAIQQXhBC/AzwIhGs9qNvp/Ig4hxbEX9Y66hP9jg4usvi4X1c1dB6R02gdrEJ\npZ9Lkn5OKxRWZuiko9oSHm8ahJJIJVcTwqmMJljmjcCbgNQpazsjnnl8h2l1j72DyEzjAzQByhDY\nVII+ggtFYOIUxgpMuXTPlwspltFCCOzdeBFbGTCCB0eW6z3DzvldirwHIkEmGSsZ+GWg3wEGgo1I\ny4iRV9jOMASiwZNpRhCaUZ7RGM8nX5/x9HZK0ZPYRrA7GDB+eMDxnZfZefpPxOsSsqMwBwMrJSMf\nYq+BlAqhMoKIpTXfoeekkODBO8+T1y/x3PueZW1ttPIKnDEE73BB4byH3hATJNPjY4KNEzFPU/qJ\notCStm1jmDY/IZEdrZpKCKbBLZOIvgOshWXAwyk2ASB42npOomH+sCK0KeP+CXePay5ePM9gbRMv\nEoIIyCSGLc66mNTzPtbt24Y2SSnrBaMixUqHTiX355Y01Qy0xAVIpjXzYJiajkjFB7SKuh8heOra\n8uD+CVVdMVtMCGhEmvLijZs8OHzI5vldfvOzv4Zt73F9931sbl5h54kRX/P4Lo0Dg6EvJFpqRFYg\nkgRUgWpLwqLBBQ9K0c4WNI3j5HgPb+ZUsxNaUzOrPE732MnAmpb5bIFKUkQ4pFqMAY9rK7y35HkW\nN8528dWW4ep4O9WH/+gr/Pkn/oDzfwz4sbd9BcBqN6SrTAtPKh1VPQPvcARQMZdgibyKo0HG3oND\nbrz5gLWdQ7a3t7j62GWyPEHTgFSdgTEsFhUvv/QK4+M5VV2hFfSEpa1q1nqK7UFKelR3IJ+w6hSc\n+kieOTQC6wOjXJI84ludmcAETFORiAjQuLCWoIoUnaW4AEeHR1jrULqgv75BUUiEtMgQ2YqXC8A7\ni3OB2AMVCVmruqKt4iTHe0aDAjNdcDhzbF7SzKYGWUjauuXo6ACCA6E6g9iNsIxMVJ5OWRoJOIKv\nEXhsV2l1bsmo3IF7+iPG8xqV9WL+AxG1Pp3j+MRyND2mbjy9C+cpZyX1bIa0FSEE8jQh1QqlJMmZ\nUYteU4JSfUIYI1RCkmqCN6w4tTrjEDdrERmKmhJf10i5QWMX2MpS1haV5tTVgvsv/h4uBNJCM1jb\nAiyurTGupZyfYJuK+zfukujAOeHZr1vK1tLagG091jjyTGMSSdvGgfMhjpcIgURE3ocQAq2xBJmQ\nDEaExqHSnBuTh6g84SN/5jsptGXvwQY3Fq/yud/5Hc73nuT+4SFNuEazWGC1pigKalkw7GmScyN8\nqFDCkGhF09bM6zHzquXe8T7e1MxmM7Ce4+kc5x1epEgRePriJlf6Oe+9fJVf/Z0X2BsHvuujX0+a\nb1E3L2IxkJy9A3/w8Q5BNHY9DyLEnaZdcO9f/mOctmTCU7oGWy4okgzhYKA813dGVDPDzcM5sxfe\npBhs8ty7LrB7cZNrX/MusIabr73Jw/0jpnXD/tThvKByjl6W8d4nLtDLBMdfnPPMk9u8dFhSto7W\nnnYy2ACVDxzOHVYKSmnZLJYshnA2hxq8pa7mFJkmTQQnM0uzDjrJ8U7SeMdsVvP5l76EWTxkc+sK\nl89v8MwzT5OmWYRRVzWvvPYK948meKUZ9nvkvT4XNoeYxrKoDEKnaK1IpOCFg4ZvGSiyRHIwbTmf\nOMaHD3FmhtT96J10Rkx1VGW6u2637IbomJEa4qPznrZtKRLJ7vlzHNaGf/nrv8ZgkPJN7/8GNjZG\nFEWP2WTCrft7hAB7JzXs3WO0vk4vHZKmCa2bkmQ9zq33aY1jXBl6WQLBIYVHKEflZ0gZMLYk1Cry\nCaywzl2/C50oTbnATg+YH+6zpeD1gxlf2m84nFsujivuvXGH+uM/xX7TMnWe5/7sn+Op972Hfu55\n+eUb/H/UvWeMpVl63/c74U03Vq7O3ZNnJ+3MRu4uw5K0GEyJkCCTkA1ZsGUYlCF/sAzb8AcbNvzJ\ngG3YsgTLAYYAW6JIQIAEiqa5pJfc5VIbuGF2dmcn9nRPpwpd4cY3nuQP51bNLE2II8MGRge4XdXV\n91bf973nPOc5z/MPN7//PfojePfQkqQF73SOtWFGZQUmBHqpQgnoOoceZAwTwbx1XBhr9k8MeV/j\nah9dsle1qkZpjFeMN0cs64Z5SBmvP06xPia4hhvXnmPHPcZie4E7eshLzz1FbR0qzUiTFJXm3Dk+\npGlaLt+9xc72GmvjNZqm5eDwIbOmpQmCt269TNFXDAc3aKuSdw4OGQ7G+OBZti0/8+LjvDDYYNDL\neae9w4XeEOsMtp2wNhrT1BVd+89opf+x8aEJCjGbllEcFc/6o48yPXhIMz+kbVqq1nHxiR7HD1rS\nsmN/WvPKb3yDMBjSJB3yuObK5U3yeYNpavqb2xhrWL+wznJvyq3pQx4cnHA6W1CohMXpETcubrC2\nNuT6E+v84St7nPhAGiJUuhVRGMQC5aqILV2g9meCWqs3vZq83juaukElisR7HnSOn7hyKVbqk4S3\nX/4+d958jVtW0nQNP3b9GTo0y8Wc8fpmfH0TVZeKNOX1/WMW5X2ch2cubXNha0TRH2BKR5YKikRi\nPRwsHBd3Ui5dFdRNw8A1cWGFKCorV52cM7ej96s8wHtEqXnnIgqSKPU1bR2JVqwNe3Tes7ZzhbKq\n6fcK+qMULwJbl3Y4PJhR+YaDwwOyo4dkSJ558hL5sGBEYFDkZHqJlIK6MwyLJJIclDhnxCoRfTrO\nIOfA+6ocgeAtwTXYak6zqCi8oWpjzSNNJEeTJYenp7QvfIxFV3MyucW7D97l4tNP0c4r7t89oJx2\nqHHCvIatp57irS//AaNTxUYKgyKJYC0b0CoeEWSmUFIwHGr2Tg3FRs5ir6bopdSNwRPIsxxfd2RK\nc7KckmwJeqofj1atYWE9CZpR1qMrMjbXeuRJyiCRKCFoqpp37h7yyMUdgk6YzWb0s4SubDFVzcbG\nJg5FfflZXj24y42dDdKtLax1LOuag3nHflXz3OAllgenyEc0+3snzBff5+Ofe4YuOJx36ESh09EH\nXoofiqBwznpDYm0F1SnVchktxFuPSHPyQcIgybjtW37spet89GMfxdkEl6R0TYfKCrbXBvTzqGXQ\nTo4JQlFkQ1584RoXHmk53j/k9cNT0kLxiQuXuHB5zIO332Iym3Hj4ohwMOfdSYsUgvaMwh3iIVwS\nlYKaEA86YvVPIYDtlthmTlct4y7Yy+hrwaVHL6OygrwoePHTn+Cp55+jMhIrJIlWJDiC8nTW4NuG\nqmx48uknmM0bLt+4wbJuaL0jl55hntETgZkQ7AwSHh9nfO2o5qS2PLYwTNqMZ58KiHmF6xYolccU\nXcT3mYSzoBBt+KIqAhSJZNk46g76uWBpYvFva1Tw5CObXF7bYWf3Er3hENc1SBWLj3maM0wH5GpE\n0R9xfWcbJwSJFDx+cZN2VjMsWtbHN1nMltyZ1SRa4YxFyRizYoExlpzkKjCElXJRYFXYw0N1Spjs\nUUxPuHdyzP7pgneOS5zq47znxz/zFD/xMz+PALqywjQNupeyvr7Gd19/jdt37mPaiifTx/nXf+mz\nTI73uPLx55lVJc1iihYBTAMh0BBojcUj6RcJ89IyzhRZcJjgmS8aEq3oOkc9nUWUaDFgWs65uHaZ\nF5/8DIm3sUiZaDrTQbAM+jnb4xGpC4zzBJ9oNodDnrx6BeEs06NjFouKZTLFdwbpHOtZgUxyNj+y\nyQtPPk59OkMEgbp6ka5umb9xi81Usjt2DKZDkmJA28x58aPPImTgi//7K/zsX3oBU09Yv3z9A6/H\nD0VQAEBE8YpMWHQw7M8nka5LwFmPVJJlF9O7rc01NnsZG7u7BJHggsJ7UFpgu5rgY0U37+Xk/YJi\nkHApzbkwzHn82iY60/RUD9e5WIs2ikE/Z3PDcn/e4X2gF2JgcCGgESsPCc5FZN+fjJnFMc7O8TbC\nUE3jKD2IRCOlRkpFT6cUecHQBrzUeBnbVbZcRLEOa0EKtIB+v6Dox53F+7h7CWup5kuk0KAUjfMM\ntGKvdFwZeE6ODYMXoKmriAKMvbmVTkU47zxIOC82KiGwK0NVpSWpUEgd3ZrWsoSPPvpo7NBsrKHS\nlOnJAq0HuLJCKUXe6yHThCTPVplHbPVhalrt6aYLllVHbTxCxTbfhdGQ42WJ1OlK/DSKf8TTo8D7\nlZNz8AjfMlCSytQ0dclkOcV5yxsPTrk/bbiw1kP7wPVrl8hSCUIiQ0bRz8hGY3SiqcqafqqZWcei\n9jzeE8hBjr+8yXiRUo01XW2YHR6ghGR9TVPXNVIIci248dQ6r3znlI0LPZr9FkEURkmFwOLwMqE2\nHcEFtsfXuLy7gTFtVKX2cd4GL0nygiTL8MuowTDq98i0QgZPTuC4qrBNh3CO5WJBXbfUiznFINDL\nFJ4c3eswBhKV0voa4xzGWmzT4ELD/l7N6bLh4ckpXgZsWaKkIuiz/O+DjQ9JUBBoLXE+UHiDaJa0\nTU1TwyBL6CcJd09LHt46wXWW8XCDQvcZ6BE6zwkYfPDMZjNMtSQbrWFtQEmFspbUOzZ2twleg/GE\nbsly/x6z5ZR2tqSrWuo20E9ynr4y4mBac7owrEtBGwR5IlFSsLHWZ+fSzvsq4rE6qPI+9d49qpM5\n5bwFb3lyu09XtnR1h5GKIu2hdEKWRq67RdDaVRFVShZdy+z0LuujEcNBgVyRYoSIVeiuWeJqg7Ca\nSeM57TxXEslCC96eGhRw9/VT+pcSXLdEp2tEDUdWxwdxnqpHzKAgL/rUbUeiPInyuOBXjk6BzSJn\nq1inGPbY2t6hrmpOjCdPc+qmoW5K+r0xo6zH+nAtArJsi+0apotTfLOkmZ/y1//DX+G//k//Wy7U\nhs47KtOQKYFxDVp6mlUWluioOumswaw8Oj8qFbrueKVa0LRTpuWCtmpoO8fFfkawlkEIXLl4g142\nIqQan7YQHCrLcHbJ/oMDfJKSJgm3p0d82lT0+2Oc79HXfY4dTE/3oW3ACoabOan01CaQKIkaJ2gt\nyZMcLyDPU06XLU4IvvnwkB6O0egy9zrN4x95lneOp7y0s45rFzhnUUKCTrA+sHnxMkdv3sQZQ+Ic\n2gvoFPvf+QFud42sNwbVp3Nz5ouSne0BKgSE6fBdh6krrHMIYei8YdEajPU0Vc27esav/+7LlB6K\nUQ8pYHMrJXGGkKRsXbj2gVfj/1tE4//3Y+XkNHeeE2twxiKkxLrAg3lNaz2ZTnAyhRCfK0VAKYnU\nGiET2roFJEmSkiY5rotW4qaLsm061ySJRHqBaQym7jDWUZY1XWdprYEQGA6iYakVkCnItSRPNWuj\nPhtbG5wXws7qCd2SxfFdcBbrPa2JajlHJ3OqpsFYR/B+pRuRIFUSPRmbBuscUioylTFavx6PQjIh\nyTKyoodWGuE83jqscdRtR9Ot7NdWPdyT1jHtHMelo61bTDOP5/CV0Mb7wRRy9dASgvfoJHpzqlWo\nS1TEi1RdF9uTCLqmjhmTkkhvkEHjjWe5mGNMh9RReCWEiLWwTYNzjqZtuX51nZ/6qZ9CS0iV5NrG\niABoJc/RikoIlkvDsj0zSPF4EXCppk3BSRu7t87TGEciA+N+gQ5gTHyNkAJvGrpmRj0/wjtPVy5o\nOkfdWFxjoW7ppAZvEL6DIDCtoa0awgovMF0uIMT74AlsXCyQCLIkQRNY1t05Ff3W6+8yWcw5mkxI\ndQSGvXrnhJt7p1ivCKhzZKVHkA1H2FXhJNGaJMlI8oRLn/oUhEBfa9bSguHoEr3iEqYB0zmyvE+W\nFugkAyUJ3mOdY9m0dM6yaJf80eu3mLexe7O1sU5KYOvqGGM6QpBkRe8DL8UPRaYgRGwzIiQqyait\nJe8NmJxUTOpI9JAhYI1jUlseHJ1wcXOdYZHTNg2TyQHWC5Ze0hrD1sWruNBhyiXNfMpCaXzXkCQp\nhUhxpqMNKbWxTMuaWVkxnZecLlsOastWobnSTxnmgr3ScXGU4NOE5z75HGs7V88BMd61mG7J21/6\nVe5//5tMH0yZLhpuTmqOGsm3/q9X+HfynE+/8DSZjF4DXdVRNzVGZVhrWN9YJ+v1KE9Pkd7QGsHR\n4TG9YhEDQoCuqjGtZVnXnFYlrx9V3Gs9AwK5gGeHCamHUDvKecXDWy9zMd0g6W8DMXiembmckRBT\nyUpJSuBlYL3QNNaTBEHnHHuTJQ8OH3I5WLJUc7pY0hsN6Y9GnD48QYjAyfFDmq5lhx20VJiqpaoq\nFrMFtTW8c7ogWM9Ln7nGf//3OzaHObdO5ySJpOzqmLGElSR5ELiVAIxWCdI57htHaQPBK4KDtjFY\n59gaDTid1zyx2eP7RzVf+eKX+Zm/uMvk/k2m1YR51/GRpx3l6Sk2UQS3TzAJ3bLh7ne/y6M7Fzit\nLcuyop2c0nYdjQ20AcrTBsTKQEhKUh/tAGQds6giTai6DgdcX7vAMFM8cmkLpRMGCn75449iWkfX\nlWgFyJihWQGuSJlWFf0MGtshrSLvDViUlmK8xnA0JkkLTg9uMrclonW4pqPfWZwNgEaqBD/0JHVD\n2XQY4H/50sssmhYbAiOdcO3yFRKV89SPXGGxX7N1eZNM/0kMhD95fCiCAqzQdkR0n3OGripZzpeo\nJCFbVdCtj7DdOwcnPHXjCovZFGM8nW8YjMekScZkYti7+S5VVbMsK7JUka2NsLahWk4xqkAoQedK\nqnIZU/ngebBomdQG6wKHy46rvYSFi8eGNE8IRc4jH/0k6kzwgYBzDd1ij+XRPVxdMS+jpt5Ia/aC\nRwvBW+/u8ewTVxkmYB0sllNcEOSjlBCgXpQIY2iajrLp0MJQDALLZY1CokTU7/PB0TlHIHDcOmad\np0ijP4IPgSKRERVpHYvDPbauz1DZ+LzTcNZmDZxTS0h0pB5rBcFGsZNECIpUY73nwcMjhv2M/niA\nlnB8+BC3XDJZVBjTsr6zSzpeo7EdGknX1nRdQ9N2tLZjs1fgu0C1nNLPNJ0P3DteMMw1hZYo+d77\nOvsmyujHhWQEgMeaDtN1SBFNe1UIZFqxdAadKr75vbd44UduY5cNxqdc2NqlNxhjqjKyRDFMGkcq\nBO8eHVGkOUnWI3hDJiS5kixXNSNvHFqKCMoCijwn2EDVepyPmcmZLO+zz1xhPdcoGbC2wtsk8m28\nIyiJDxZ8QElJdfIGQfRx3lG1jmUbEKnEVBWzec2i7mhFRZ1JFs7ipWNR3mG4dpHWRSSpCRYXLItS\n89Zeh1CK6bxk0bTvSQ72Csb9jETAKB/T+CX9TKHfU8X9U8eHIyiIM4isx9qOtq7wXiBUyrw2XFLR\nPESgSELgzoNTTsuSUSYjGk9LFouKQnq2dEaSpWwOx7jtXZqmpqmXLP0SnSbMymPWdsZM757Aisd/\nMCm5M48SHxrogFRKThrHKFP0x32uPH6DYX8YuRTe4r1lcu873P7Gb/Db/+D3efveMXuV58mLA+4s\nLMYGNjLB7QcPuX13n+ETN5C2xBDlp+tFSZ5rmM6ps5wiG5IlA6xtsd7S2hZnHamUZElG3dY475iW\nJYvOkasIwxZC0AVYHyTUElTT8uDmW4wvv8p2vrGyxYtN1LN6QgBkoqPnxer8rleWmkJAL5FYC7/z\n9e/gJcg0IUjFaDBAac3O5a2IbkTgGsvSV3RVgymr2D7uGoTwCCVYzPb42ve/xfqwT8DzxMU1uq6j\nqTu0COfHGTiDYwfwFoHHWAvGUe5PSft90jQjT1LmfslGL6Fx0YZ+ejLnH/6Df8Rf+Tf/MgMB+WCE\nEJLBcJPPf/JHeHD7IosrS5xt2HYtr9++j0hSRllBZhLy3oC8qZDWIZyM/A8fcA6auUWoIWs4AAAg\nAElEQVQBr7x2iPGByjockPvAumyhsaish/dgfct5GXpVMPVECHI6eBQhA/snJyRaMSuGTGZzvv79\nr7KT9+kPLmNPpsiuRYmOLNWooNh7sMdsNiXLM/rDPjoY3rpzyGnTMK8aplUbRXMFJErxoz/6GbZ2\nt+lcB0Fy7foOuyqlO7z/gZfjhyMocC4BAAGcj2IRxsPBrET2cxyCjULgrOCk6ZjOl6RXLjHcWOPl\nr3yVOz+4xXMf+yy2bBhvbRCAZbkAPM5Z9qfHCOF56RPPkKQZmVKQpxjnOZk3BKKl2kaumbaW+42h\nSBVV19HvFwzW1gimAZnEtNc7qslDtNIcLyu2tgtefX3OvdOaVAm2JMwcEDx7h0d89JmnyDKN8Jbb\nP3gVd/OAtWuPML+/x8WPPYcHTGuoqgor4ehon93LO1y9djniB3T0GjhZlCjiObwhsCajAkU+1FGT\nUoDpOqrJEaatzoNAxMOzKi+Ic6RePughjGV5WqFWLMFk1R48KmvuHBzx5I2rnEwesjXeYpBnDHSG\nbxtqY2mqknxng6asIv5AsIKXx2t/5/ZNbr55yNogp5crbt2vwYPzgc779yjzq/d2Js14xnswONZu\nXKY6OYDgCSIyZHt5giQqFvdSwXS2iExYa5g9eBfaC3jr8GgGvTHzyZSurLlZLWiEoFi1lYfSYZoa\nrSTWRdt6a/zKCCfQdY5ECB6Who2Vurb1IXrAygQFNF278ll4L8vhjPIdoqtYlGvv2N0e0rkGQwAl\nGa1fZW0wYJCO6CcZDg1NSdsscW1DnuW0OoJMBkLy4KTiZFZxvIjQcakkWkX0qhOej33soyRJwvDK\nFTIR2JxFaPm7y8UHXosfiqAgVn+e3TxnLW3nSQVUZcte8Ix1woLAiXW0TnDnwUM+/vxTBBxPPPc8\n271N1nYeY/3RHXzew3QtBKjnJzSLGYevTBn3cy49/RHKg3uIROKMZFq3HJcdUgj6eWTIKStIMw0E\nWhNQiY7tpGDRMo3IO2dpZscsZzNuPL6DMB2X9mqq1lI5QaEVNy70GI363D08psOTycCg6PPo08/y\n2iv3MfcXXL/yHGK4Sd205LkCecLpbMrOhS1uPHaNwbDPcjrHS8Gsqrl3NI16E6s7VjlPpiRSarK+\nxltH1xmaqsS2dWyQvI9XAqvqsvcQLARNPy+YiDoyEUNAxlMa1nne3X9INhzQKysO7x/S5H3mDxfg\no817Zw3ZZMLm9gY6yShnM5SU1K2l6yzv3rmPMpphX1O2hkQLgov17dVp8Ic9KM7gzavdWgiFUxlW\npNEpWkic70Dkkd8iDco7JI75YsrG+gaDwRrf+iff4ZGPXMEmKdOpYf+kolxUHE1ntNbw/JPXSdKc\nWTmjs5EOH92pziTUA8ZBWbUYH8iUPGeQnoPErY2qUc6tmGbqzNX+nFh2BsKKxrqSja0tjg6PaRrD\neDTg4miEBnxbY11AJCneBVKVUoYKKTTr20NCM+CtvdscHh6zaDpOFiVWrFaNiEF2POhz4+oOWgvM\nbI6xLVle0HYtdfsvmOs0rLhBISorz2dLTsqOYDy76wMOFhWpTOJEdJ7Kwu+//C4vPvcE18I2G+ub\npE8nzL70Cu7NEcXVDbTSLCYz5qdHlKbiE59+no0rjzDZ36dZLGlM4GTe8I1X7nJnVkdgTxc4qloS\nKRgOEkxl2CgSrPcEJQlSodIkVse7impyymNPf5r6rQPu3b3L+ihlZALvTBpkpqm8oVvOuL3f8Xt/\n+C0+/bGPcHl3i8H6kBf+1T9LM21wd0rCdx9SOENtSuxuwsZ4zCPPXqdcLmlay3yxYFFZvvhHP+B7\nB3McccKq1S4bQsCWHWu7BYtGoHSgmc+pZ0cEIVZcxPds7QLx6BG6DmstDZJernAyYDwsO0dvRfJ4\n9+Ep3371TZ559CqpESgnGA0HUZ+wS+gnksHWGO8dk9mUtm6YLJdMFktM17I4zbm2s8XJokLqlnKp\n8M5HIFMIGBuFhtSZp12wQGTCRhiTBJlEdqGP2ppGxozIS0Wvn6G8QwnHF3/r/+TzP/dnuHLtUX78\nly/iWljs75FsDqiPCo7KEjEo2N6+wGBUABaEJ0ll5KWJ99iQzgW8EEwetvjgKVa2GkrA3HvGSuGc\nxQZBdB8H7/25JaEQasWjOXOCjp2ZS08+xem85fsvf428KHjy8esURtBHkgoJ3hBycDbQ728S8pRT\nF5iUJ/zg3l0Cgbce7NF1lkKl5OMejiVXd3f4t//qL6LbI+YPT8nH1wgE6olGKoc9U+r9AOPDExRW\nPf+ua1gua5yNooI5ghxB1xm0SlaeeILaer7+zTfo9XL6/SE6UaTXhoQjy+KNg2iquZuRbPXYGq6h\nRwWL+QFd29C1LbN5ydt393htVlFajwkB5SPaT61SXyWgCxBWYiR+ZRvunKFrFwhTQ1dxMJnw9sMZ\nrfMUwNIGdOuomsjJdx6+9dptdrfGbG2MCVIgM02xUdB6h30IIUnIsx6j7SEi0yzrJQ6HLzvKsuPo\n6JS3T5ZUztOsiFvifWl3ah2zaUs2ShH4aHXumnhvV+DmMyrxmfeld5BIiSbaw6UKOu/ROjJMk0Ri\nPLz82ltsra9xfaMPXtCGFiElvdEQlaW44DDeEYSkaQ2z2Zy2OmF96zJKQOMb0iyls5686GFtQ91a\nGuNQq4LCe8fHMwD5+/klZ+AKj3QW5VvaLmMw7IMyaC/IpOTo+JSbr7/B7qXrjDa2MPMG62L9pz8o\nqE2fFE1vVKCSSL2OCzqcA6a8i49m5RbVGb1CgAZSKRD+LJOK2pKrqRKLt/79BLn3SV6t6PEEh85T\ndq5f5tadO1ShpjM5a2sD/PSUyhqc8yRCIYDBcIulq5lPDrizf0hrPZPlHHwgV4q814ekwKqU559/\nho88/wzl5IisqsmKHj448nTAWmJ5uGg/8Fr80AQFT2xNzU8PWUwnWA8XRxlvHiwYak0XgDaaZ6gQ\nz61/7xvv8KUf3Oe/+g9+mZ3NDTZfeJxEp+isiFh6Y7G2w9qGVhXMHu6zmJ5y7+4DvvmDu9x8uKRd\n2S8roF3xAhof6IxnbTOhOjU0dUvXNvgV78GbEtdVtDrnj159lZO2JSQJUiqmneH6OKPsAgsb6ElJ\nB7x9vOALX32Fna0RO9tbbKxvIRPB4JEx6nGFEI7gAibEiTyf1Sznc2bTGQ/uH/LO3gmTlT/CSIDQ\nglTGySoTxYkUXFormNvA3JSs24a2nJ2byia5RueapjTgA1megZRoFGu7I1wXjUfePFwwSBSd9Sgl\nwQtu7h/TfPEr/Pt/9S/RzzOG4xFCStqupW1qZrOK5XzBrXfvYjvLtauXCeIyzkhqGkZrGX5Zgk5o\nbQm+o2oUnVExaAZQUqys7MO57qxgJeTrPd401NbQiUBvOObWwQlPFX3GvSGWhl6iqCrP97/zKv28\n4Cf/4r+G0AnZhTE2aynMjEHq6EyFs4a2qnCmQXpL8A7TNnSdxYcYFBMlWNaOdG9K7TzbiWRuPJYo\nFJwP0nh8ECBC7AqdBYV4fJDnClbgCbZGBMe8aSjGQx555nm+9eZNvvLyd/jI1Ytc7PfRaY7OUloC\nMuvz4PiI2eKUVx884HQ+5+h0gvawM8jJiz4v/fJfppwLfvV//dv85u//AX/wtT/gb/03/zFbV6/y\n1V//NdLNXVJzmard49Ef//QHXosfGvCSXMmwVfNTTNvQl4JgLMEHEgGFFNSrtookHuEKKSk7xxtv\n3WQ2mSCzAplkpFlGmvZRKkeq2FJaHO9TVkvuvfsubVVxea0gl+9RYhNWGcJqeLtKIQFrPF3TAnpF\nRxYEpZi3hlYmWCRKRfvyvpb0IFbWQ2BbR2v2unOUTce8rphMTmjbFi8jbVoKgRQaIXN8ZzF1w3Q6\n5fDhAdPpnDRR6BAdm6M7dNytojaBoAow7QIbu70IifULvA6YpvwhngNCsrbeJ0kUg0HG1Utjrj2y\nxdbmGkmRkeZZlNRXCqWjonAg6jCcLiqqssY5h+ssvnN0dUtdNhweHbG3v0cmJZcuXWIw6KOFgmDQ\nyqOkItWaTKcURUGRC3qpRHmLlisqNay6UPERWbOxu2StwXUNBEtrPYM0pd9PuXU6ZdYauhAQUrHW\n0zjTcuvNmwQkQkn665sMt3Yohn3662OUsATXYtoa1xmMMVFNu4vGKkpEBfHcw3oqaZuOQsZagxJQ\nW4+SEm9cLCRyFghiJ4RwdrfjEUh4S3AuKkW7CGUPwbBz8QIXLm7z5Zv7vPlgn735nCbJ8BtPEtKr\nOHeZ/emCo9pycDph/+SUTCUUieKZxx/lx3/6Z/nISz/KYDRgvpizrCqO5zV/53/6+7x9d59nP/9z\n3HjmOUZPjNGb0S/kg44PR6YgBN61OFNz+M7rNHXNiXG4qkVIuLae0lq4PTPUPh7sCmBcaILwXN/Z\nwmP53ivfpDces72+ixSCqjM0dcVycsL9268yGG/x1PNPUS2W7H/tBxy1BkTUSmhW1eZMCjIpkCEw\nnRqUDyzmNd1kiqkXkClUNqLwnk5lPDid8oPTCr/oSBKNUJK2aRgTHan2Ok8uBRf7CY1x2GVFtjZk\n//4DOmcYbW6ToFFSYq1lPjulXM4JrmFtcwexbnjjrbvcmZZRSwLIVlJKUkKqJWuZ5HDZ8q3XHvL0\njS3GvUdpa4vpyijWIkKUE/MtxZrm6Wevsjbqo2VUgGpbx4WtXU6rCp8eU9qORBSsXc5YlynHxy3O\ndHzh5e9Szvd54YnnUEhMZzDWsjHqs7OzQ54kCCFom4aAI8hAaC1KZ/TSFCktbT4g2c7xPAA87cES\nS1THsjEtiDusj0rdvimxR7dwxw/wJMjEUojAta1tvvJgj5NbDT/y/OO4nkK0DRc2FbPpId/+nd/k\n6lNPkuUpHoca5CSuRSpHngW6hYkZQ9vhXORmaCkQzhFC4KhxbDyyxezhksXdU4IQKAk7mcIaSxEk\nTVMhlEIIHY8Tq5qNkDJmCji8N6tW7wqpGeD2Ycl/9rf+Hj/5uRf4937xZ/jSH32Tb0zfZPt0jpdv\nRHMeZ7jz4AF126KDYHO4yYs3rvHTf/6XWL/6PJPW8lt/8Nv83m/+Q7x3KydxwVe+fYsvf+tv8+iV\nXT762BX+lb/w89x44ufouj/ZmuBPGh+OoEAkDwknqaqK1nkcASUiwGVWBVoBW+OUZtJhTUxtn7hY\nsN5LUCqwPh4xGgw5uv+Qm6cv4x2UpUP5jkQFnv7kZxn0+/i2jjddylW9YNW3X0maR+v2CFbpfGCQ\nxXNyWdZRgltpQKJ0zpWdC9y/eZOT4zm7wlMGh0Sw9KBXoKLSefoqQUqBVpKqbmiaju2dC1RVw/Hh\n92irfuzmyZQQDP0iZffCZbwI1AtD5z2Nfc9W3oZV+3Q9oW48mZZcKjIOFhXXkDzxqcdopquCnRCr\nbkOUPdvaWqPo9ymyLB6GgyDPUibzhq6OfhKu8QgNJnhuH5TkWmFllG67/ug1xuMRidZoKdFSRran\njxLpPgSadhk9JZAonRGkjMYsIZDoGDjGa5sIkXE6N5R11CN0vAfGEiKAqVDtBLk8oVcvccYhCCyc\nYG29h9mXpAXMyhKRDehpTSYdgxD40m99gau33+bZ554kzXN0KhisD5k/TDHtKVJJRFA4HSXapAyx\n8xU8rYR3g+P6/RNM40iUoFn5Z2it4s4PdNYQnEGhwHerQC0gCJSKRsQhxIqUswJj4eW33uF3fu9b\n2HLBye19bnzuk/zMx57lS1//fQ7vHOJCD6eGKJ0gjWe3P6AYDNhY3+AXfunfYnT1Ee4dLjieLbnz\n9pscHxyilSKIcG7G7L3nzdsPePvOHm/c2+cXPv85XnzumQ+8Fj8kQSHgTc306F3msyXBBya1YdxT\nIOFu26G0YpDlPPPImGeeusLGzhp5Ct2ypuw6qrri2vUbXLx0MfpPOo91JuLErUVmKd47To8dQSh6\ngx6/8NmP8He/8F28gCaAVgIVT7LRCFUKsLBhA+2iRKUtoVtQL6YonbB19Wk+8Ymat770FdomUEnF\n3rTGCkHtIcs1tm4pTce1zSFrvQSHo2wb1qxhOOyzvvZJpIyVaoKL59MQqOuarm2oOsfJfBkLgDIe\ncRoXE9SmcggpSHqC3AsaEbhzMkdpz8blAfNJhRKxSv7SRy6zvrtNnsZMKFUS4wAk3nvauorS4iah\nX+QgYX6/IS8yOg/1YsnpbMILV55gd31A0zRAvEfBOIxxCJ1g2xbvE84dAKWmSFKsb8gSRZF2hCAo\nejts715g+8Iu9+4ecvvuQ+xKt1EKx/79m4xdi61mnO7fYjGdMy1nVLMT+ttXKTP4uScf5bWjQw4n\nEzaGGUIpOhLEhsBXHXffucmdt77Hzu42P/Xzf4400cjlNWZHOZmcRlWuOvJP6i5CqF3tSK3khV5C\nWRkS6RFaUthYgDTO0tvqYX1ACoszFnQa57DT1LVjvljy9e+9wv39h9y+84C6aSnL2PJFRIXxNE24\n+eCAGy++yJOf+wmuXFzj6GhCsJ6maQhoJrVB5wP+/F/7GxT9EW9//QFv/sZbVJcCb9x6jW98+Xdp\nu5aQEFvpUtK1Hh8EclWs/P7Ne3zvrV8jUR98qX8ogoK3huNb3+b1V75F3VmaEOjlCSoVBAtGBHq5\n4pf+7KcYDPqsr41QUrI8PaY0lmXd0nQGGwKpVnGXEbFwhQeVRFGQMx6AlDDu5/imZZwoltbRuBDp\nsD5ERB9EV2Ni8ctZSzV5SDJYjyQTBFJlBJWTyAwhPLUNLIxFqyS22SSMeppJbdmbLymyAic9rV1i\nnEMHj1axM3BGbQ0ru/FABHF1JpK2lIyQa+khlytJNRdTHW+jbPijmyNqY/nDL7xKngm2djbZHhT0\nBynjjQ1SrelJHyXsrcNbj850PDcXKV0VGKZpDMA7Q9q7x/QzTdOBHg5ZljMWbc1FH0h0lMcTK14F\neJplhfPRqQhrV3UMRfDuXOylX/RACExwBClIez2yvEdIUrqqInjDfHaIDIaLwz5v7t0GX2O8ZX96\nRC+NSlPCB9JhxpNs8erd+zSuoi/7Ed2XpHjRIFygpwVHxwts25LohNFoBMbgjKHrOroA0ge6UOGF\niGriIupl1j2FXAQa16FinyLu/gnYNoAMJKnGec3p5JgvfOWfcuvOPkcnc6azZXQ3O8NiKB0zUlad\nFpHgCdy/9zZPffxH+Nhf+HOcvH2b+f4Je/fu4IPkycdeYP3KY2xcuow1nmyUI3opiSh557t/BN4h\nlCdBgI48Gd0PuJKVqjkooaJ48Rkq7AMM8c/z5P+/xvYwDy8+sU2wbcTDJ55LwxxXl/zY557j2997\nQOJyfuWv/BwOS9VavDU0zZK2rHFSM0pH7F68RJpl0XwrgPfRZUdKwWT/QZTo9o5qWfLw8JD5csne\nwUnUcehFK/e6c5wuGt7Ym7FsLemqNZcqyYXdAcXaBp/98c9Qlwvu39nj8WfXmJzc4au/e4cHJy2z\nugKdMU4VwkI/V/T6KbvrQ0bDHtujAaNen+3tHdIsI8/yc0y/ECEuVh+YL+fMFwuOTyd894073D9Z\ncuekQoQoqSYFbOQRI3z9sYxy4umNctJEkxYpoyJhpAQP2hFBa4o8odCSTMV+dWcdnih6cnx8yKx0\n+LTHbOm5srnOsTR85+W3QcJoMCaX8MSNTbb7KT/50kuAoDEW7yxlOafrDJ1RCNTK4yGCdbwIUT1b\nODIlqVf1A50laJ2i0verSQcuPvkEuSvZLMa89Z3fw5anzB+eUNYtd6f3CInkav8aUkOSaBbzBcOi\nj/UeawxSKVrjKMsOZzzrmYYg+Il/6SfZuXgRrRK6umJ6dEzdNJTW0NYl8+mUrmtZzme0rY3ZQ8+j\nC8XrL09pakvtImmqd6EgzRTH3QYH+ycsqggMOgvmIbzH6YidCFYwfrkSlpHkvYJEKzSeKzub/Oxn\nnufG1TUubW+TDddAp+w+9mnWNneAlPm0ZHLS0Jma//y/+E+4ffsHtK7ByxaLjsHYBISM2YjrVvUN\nIRE+otdOJsffDiF84k9bjx+KTMG4wPMfe5orz1/nH/8Pv8bpYcuLn9/gI488wcdeeIGrO5t89/sH\ndAGyfAjN6bkYhxeOfrZGkeconcJKqMN7F6vZAZyz1LbGGRG7E0lKIjXjXg+3aVBSsXVhM/oYaMV0\nueAZ6zmaTilbw2uv7tO2jrfeOWaw6aiqLzMsNIt5wydfusL/8ZU9JvOWZdPS2MA48xRaI7RAKk9/\nkLG9Oybxkl5aoPRKKTFEbYWzEXzAWoe10UHbrB6eSA3WKl6PDBHea1bXVzuBHGjSywmZV7hjR4dj\n0grk+sZqVkpsAOEAIei8XFX6o7Dqab3E1h7pMpxoI2rSenqDHkoIjDFc2NjCdhMckGiJN1FevDEB\n5zSlacjSQaRcS4XWmrpuKVTgipBskvCWknQi8lpkkpPkcXcXeJRS9HoFu1uXUZWhcTXd7F0WncKa\nCmcCMnM4LApFSFtqUdIPffJMU7WRx9HVhp5OKE2LUpGm7IzBWYcWkbSU5DlBSlxTIUJOnaaE4Emz\nDCGgLDv8FOpZy+WrPU4Oa9pZh3CweNiglOSdxT62M5H2f9ZBOatSvV+MZ9VZi3+PX5W3rKcJs7rj\n3sEJv/7b/5Q8T8nznM2dDUKAZ556jT/zkz/NMx/9BNa21DYKq9y59zadNbQhEJwmCLXi6HmCWWF+\nztu7gX/eff9DERQ2dta58dSI3/3Nf8KPXRvzwrOX6D3zFHk+RAGPP/EsN248zeRwSjlbYnwXT/4+\no5cV5EmGVArXRcel4G1s24nYTrOtoaaHSmRszYiE9R2HNR1Ff4CUguHWZnSZEoHBeI3OdTz6yEVK\n3/Jgf8Zs2pHqDGdhb1LRHcMTzz3Bf/k//hZFVVJkmivjPJKqpCDLEjbGA7xQjIqC9XxIIqP2Q6qz\nCKPtbHT2ESt5c+fwHsqmwRhPaz2VdehUkyrBsKcJAmR0YmX30ZzEC5aTDtN6fFDY1pN5WEwjBmFj\nJ8MHj/eRBSlW9vJKRRu8h/sPGWxdYS2ccvvggEQrynwXY0/Y3dkGIZhXC/pJxkavx8bFHe7vn7C+\n3seYQNtZfIiZQZIMUWlB2bWkSQFKkY1zEgSXUoVXOgKGhMKpFKV76N4QTYQBEzzFUFFXC47uvYlr\nLeQXYe8NZDrmkc0rCClQqUbJBNcGRJPTYEBputYgAgx0bB1eulTAUuIaUGhcB3XX4L1FF31EluMT\njUgTiq5B6ug/YU1CXuTky5K27pi2luuPjRGLmrtvLEkDYMKKtBfh8Iiwaq2uXMpWOIU4xAr2vJKX\nCyDTnIeLhlQprJRMXUCUFpYVt45KpFR85619/vGXv87f/Tv/HbujPkc/+Cq/+mv/iLZpUEAiFR5J\nZ+wqExEgV5tMOKOkh0jKEh9czflDgVNwJjA9OOInPnWDwbCHCQnbvS2GLkHJAt9apIOsSAnCs6iX\naC3IEkmWqZVuoARh41YoIXqpROIPqaKrFqR5hpSgtaDIc/I8o1fkZHkeW1HGgHVoFCpovFFIMeDz\nv/hjfPannkdKSaISpkuLFimXrjmq2qLTHCU1a5sDiiKjV/QosoIsLdgcDOjnWcQYSEVwIWLoQ+xl\n+2DxIdqYBWdxzuBtTMuNcREr4SVSJSvzmGgPJqViuud4uG/pKsAr7MxDJzBeUXtFTQyAKkikA+EF\nMsRIOSwKZNXQ0ympTslEwkaxzrz2lKcnjHcvROKS9eRJjzRJmS1rgrVIAqZusV2LtYbWWIz3SB3b\ncFmWInU8x0ob2HQeHSStdSRSkSqJFiC8I3QG5TyJVIjgWb57h+PbN/FtR9d0NKVFJuv00wyEQgmN\n8ApvPaYyJEHjGk8vTfAGrAHXCYKVDHcu0xv0GW+PscHRdUuMKXG2jaY2K6CUlopUJaQ6IVsJ9KQ6\nRcuUJMlQVrLcN7QPPT2tsAi8FCilEELyHopR/NAjEqNiBui9O7egE0KQj0cgBI3zgOT5T32S4KLo\ni/CKYAV5OsKUjsHM0G8MX//Db/LGwR72TPA2cP65xvqSWJXJxXlG4j1IoVaQ+A82PhQ1hSuXL4Z/\n96//ynnaE2FiZ9F15V0QgPdBdc8ObX/cb/D9IyDepzwUVs+PbbP3/XT12vdedeYQdPY7nYvn/M4a\ncC1/46/9R6RFgl2e0i0WhOkMZzu64OntXuPyR19EKokPgdY5AoKqtGA8/X4awUjqnJ/0gcbZmVu8\n71XvXe/ZZJPnEObOOh7cn/DyC9ewPjL7XIBstXucQWwcUbF6AtTA6errknD+MwWMgBsItoDLAnIg\nf48oTKzpivPfe9bqdZzH6PN3fvbVAi0xs26lwAr4tY9/Opr3vB+rv7omVgQgsfIMPbt+5330Yuii\n7kLbttRNifce0zV4G81rhJB0xiGVot8fUhQ9Ntc30Sr6dsrVMeA9X1PeYz1Gtl7ceX3kXbx7+xVA\nRp0P7+kNBrRltcIlrFL6lQnv2VxefXB4Z8nS2J6N1PW4uUXdyvcAXLHgHOejcxa8Y5QrmsZwdDpb\nVTElWklSrXnsyiV0olfAr9Vn4QPWWb74tZf/xakpRFLP6gNHrKrZZx+G+OPPjF9XGAMh3psg5zd+\n9ZrzSfi+qo9bfYBCCn7Igmb15Pi5nQmhr1gDKzszcUbScR3B+qjDN59CVUFnwEs6tTzfJySQIPEB\nepnCioBUMlrkrd5XcFGK/WycXdPZ9//PcbYEf/hHAiJoZnW2TRLNqNcjuIAOkIfI43hffI2CJatH\nTpxArRDMgBmCKYKWQAroEM4h0zpAiiBdXefZ8vWAQuDFe9LxZ5/D+90yCGBXVxfrIuFcVFZIEaXr\nzu/+2evCe5cefvhnMoKM47NFVLgKYWXF56NlgAiezjmMt7FNu6IzBnG+B60W89lGchaAw/l7Pnv/\nUZ/CEcLqfocofmutX5noxt8VVjPp7HvO5ycraT4VNRelwODRQkesA6u5vHoPZ25RCg0AACAASURB\nVHNcSLnikrRYZ7HBgtCEEBmnWgqcfE+LE85X0R9fRv/M8UFs464C/xtwgfhZ/88hhL8phNgAfh24\nQbSO++UQwkTEGf03gX8ZqIB/I4TwnT/t/zlbzD+8EP54K+X9xZv/m7o3jbEsvc/7fu9y1rvU2t3V\n63TPTs6QnBluNqnIWqkF8BLYQhA5UuIEURI4QGIEQYwAQb4Fzpd8CBAgMJIPTmAgNixFUmBBSqKY\nFGWQErchhxzO0jM9vXftdbdzzznvlg/vObeqh7TdAoJgdDDVdedW1b3nnvO+//X5Pw/dmgidIvCp\nokH/J6cb/cymExGQKoNcvd/Z4C8IASIgQv/eISLTOqPlg8DPTzAVmDAH7ZDORGbkZond3z3TdhIk\nMraiMiGweYKxnUxcv8hC6EaEVx9vhYw7vQdnP744/f9w9ps4/R0RZeHKYYIMndpD6E1geGyB93+i\nu0c5MDrzQm237UcCihDIEGRASkCLx1Uk+m0kwilB7IfXouzOWXUbvjcWKvTG6tSgh66Q2l+EXhtT\niJ6yqdOj9P17RQODjxwazjmcNUDkX3TdMIJ3No6/9xGjOHuyq1daRatnYjNOS4UCHzwyqFVdsW9Z\n9ob5NIkIq1D01MzEaEH6gA0B71qCyEGkHWblNCoUdHl+8MgQIjrRxs+nAaE1eIcUCUpGdOxq3fRr\n+M9gFZ4kUrDAfxZC+LYQYgR8SwjxfwH/DvCHIYS/J4T4u8DfBf4L4JeIwrLPAZ8nytb/K6cxQr8r\nwuMW+vHFG043cO/u6Jl7Vpea3pg8nk2ElfdGxonHs8Wgsxutjzx6LsYVQWuIvd/dm29QjEZQGwZJ\nRtGACgoZMkxWrBZA35tHdkKvMs7feu+RXUTkCQgl8SEuUm9dV284NWZKa4SUndwaqwXcF63iOUbA\nypmLRT5IGISoI5l3V6YP6U33a70eE0Ty1iYIVPf3pYhirGsBRni2CYwJbAEFMWII3Ws5IbD0cxY/\nxjB3//juHuru3kZUqSAVAhf8Kv0RSnB2O/YV/EjYG5+PatSC4Pyqk+Oci3WOto2TssagZTfW3BnV\nEKCuYxuxKAqKokQnSbwnkZ2RiAQ9mzpAr4O9iiRCZOGSdOH9coEU0XEIqc7GGWcena4rZx2Jknjn\nQKU4IcFa0rKkbZro7YU8XYsd9NvZwKKxaB1l+UyIEn/DsgARcMHRm6T+XaV48vLhk2hJPgQedo9n\nQogfApeBv0oUngX4B8CXiUbhrwL/S4gr+utCiHUhxMXudX78IaLOYfzvRy3ah70anEkJVvl0d75n\noomw+u2w8pQQvVS8Ro9b48c+9+rzRwPUe3UIlElOlo9w0iK9wAkb1YWSgiCi7/ux50zETlhngRTl\noovx3jE5PORod59Htx/irKMsclSiUEqxvrlBMR6xcWELqRQySToWpd58hs5xPl5z4EwY35uLWNY6\nEwp3f6uJg1uKQNH9/hEBJwSeqN6SwGNfunu96CXjduprFKejQqcX8mxE9uFrE+MxVoU7gTxTU1rF\n9l2u3RkFF2HafkWf5rDOYqyJQ1Q2AsREiKA1rSOzd6w/NDhnUEoxHBqyNIsy9KzihG5f9Y/7COJ0\ngQTfEeJ2BUQfAkhFCAEtQhfpnPVMvceJr+18R5AjBE6IODClNG29RIient93dAFhNbrf14gQAi0F\n1se9I9Xpxn8smPwzpA7wZ6wpCCGuA68CfwJc6Dd6COGhEKLXur4M3D3zZ/e65/7FRgHR5fjQb9TT\n0PP0+dMwtdsEosv5O8/aj9qufrubbjstRHatP0GEQnPGloYzV64L6R+TQ1+dKah0iF+2SDUgSI8V\nGusMsqkxZ7wkZ793odysecC0mnIxf4agNI9u3+G733qDr/3Jt7A2YIKOBUOl4nyBVpRlSpGnPHNl\ni53zF3jxtU+QD6K4SOgwCPF9Yigt+vDxzBWLMUqMlE7D9rAyEP3YeOh+pyYw1xlCj5DmBO8M68RI\nYAQMQ6DklF8xen+/Mi6rIma3D/yZ+9nHdH09w3cvEAQopaJE35l72edWMW2I68EH8KE9U2x0nEwO\nmU1PqOsG5yxFkTAsNSJ4pgeO2pypf/iAdXB0uMfx0T6T4wM2Nja5cOEiSZIiQh8ndDL23aqMa47T\nKLJfK11hsC8Shi5KfCxS6q+BEIDC46k6gljVEaykq5TFc3R4TJ4X3Qh2lwYEInpRxErK0gdU8KRC\nkqpkVRMTZ96/xys86fHERkEIMQR+E/hPQwjTvhj24371xzz3I2ckhPgN4DcA1tfXfuTPz3rXzgLQ\nJ8Orxb7asD5ivftwnS5P7IJVeSZS8L217s7qR9KHD73/2Zx5lSNKRWNrlGrjBgwWKQMCxWyxBB9+\n9HV9vDlH5gHH0wN28qeZT6d84ytf4wfvvEvTQBAa0zpcMHghSNKEREqctbS14V3Tcv/+AdWyZuvc\nFs9+6mMkWYbSarXRxIc+XO+YusHe1Qb98M1y3VdLYIrgEJiVBUFKghiwbAzKewYisilvCtgmFhuT\n7lVk6KjVzsRo/fv2PmxlHzmtcfSGSRK9nfB9xNBdc9nHM/Hm97wFPkRItXWOqp5zcLCHaU3nJATW\nOaxXJIkiUZLW9Wqa/fVhVRhcVHOcs+R5wdrGJkonaHFaEO7P+fGNfupwHlv04XRhra7E2QURoiNT\nMkatZapZ2Ii+nDUGrVKqZSxYL6tqZVCljK3GROl4naTEmvh5+5rcY90SuihL9HJ8T3Y8kVEQQiRE\ng/APQwi/1T2926cFQoiLwF73/D3g6pk/vwI8+PBrhhD+PvD3Aa5euRyk6C7+Y+bjDP/O6mdninK+\nsxUO8FGjUHQMwWmiGRYDvHMYE5FuQurIj+/Bd/UE2wGB4pucRh/9jZeI1aIQgjgPn2+QDTVJlmPq\nmiQbkSnN8ugh7lCsFsVZlFsccppw8OhPGSye4Z37b3Dz/ft8+83XuX9/l82tK0jXUkqNQ+AUKOUR\nMgqJ1Nbj54YDY7ize4RSkgtf+RqXr17kL/38FylHA6RqURq03lp5kqS7jmcDSyFC7HEjUN2ylQIc\ngkfAgRDMpESMFKF17Dc1w8GYk0yxmeZc2nkKxlssg6KeHeIevI+sK9bncwrvKPriWbfAYkoR7+Wp\npnQ8+kghzp5CqmM1PRqFbpF3kY/3MUQ33uCCo/UWYxreffsNmrrGn1GoFQSWS4uQge1xzrntAiPg\nwcPFamUFIgUgxEijNZZbH7xLeN9w9annuHLxGkrp0xYhp143hDgVGgNMidIp3hoQkaUrFpvFyonF\nB+F0w0rIioT5tGKxaAhIZjayVxXFEOPgZLYkSbpSrIDgHAHIrCbNEpAC5wVZPqAYFHHOwftoRJ0n\nSzS1aQhBEP4MiKQn6T4I4H8GfhhC+O/O/Oh3gX8b+Hvd99858/x/LIT434gFxsm/tJ4A9Bu9z91E\n6FFhZ86jczGnKUbvRSKhCc6SyOhVyiJnOCjwTUPVNrj5DKkThoOSRbNEIiNNV5DIJAfkKnuIJKKC\nHwmEQiTfCB68hq1Lm9RC4fYCZrokhBYrWmSS4Z1DOLGy0vGMYdFMaWeW3KW4psUZybfe+gbn022S\ndEkwNXkY4YJm2YenHqyJ2gGNsQgv8UGwdI6qbdg9PiZJAtdvXObq02tkA4XWW6dFSiG67kzv7bpH\n3QLtPbkXgkYIjqRkKjUmSUgGGaosWEqFKEouXnmajfM7iEtXqUfriKBQywrx/lv4R3c4fOt1hk3N\nWmtXBr6b/6T3++LMmZy9xKuCmJSdzqU69XyyZzEKCN+3cAPO1BzsPqCt68iWfBrUrd6jXjoWFWSh\nmx6UIuboncfuaxgqlZH5ykbi4IPdB1zY3kFJ9dh5n67TMwGnD2eDg1WqGOB0La/ik+h0osp46EiK\nLVJqFsuaLMtIvAWtGAxK2rZhNBpTtzXOtBEU5gNta/AhQuJ9os9gLGT8aAhcgCJLODmZkebpv2Dv\n/ejxJJHCF4FfA94QQrzePfdfEo3BPxZC/HvAHeBXup/9HrEdeZPYkvxbT3QmoQ+SzoRjoU8D6AzC\naT6nVYrSkeZKuwVpqknSFK0Ui9mUew/uY4whBIE1EZ/+oG1x1uJMi3E1KkkJ2SaqGJCPNqJKVQc2\ncXSV7dBjFTzGxO763mJGc1dR5gPa+YJhXiBsg6gritISnCF4QQiaPqOJxaISzIjjRc3m2g6f/4XL\n/NE/f45PPnMO27Q8Or5HLtehHeLa6D8ba1nUDq0yHEva6i5p8UmED0zqGoLn/r0HpAl85uVn+fQX\nP8tLr11A6QFCqpjfiw4/0N1w14WaEqiloBGKO1nO3STDbF6kLErQkI5SRtvbzGYH3DdHXLqySXH+\nHLMsYVYdQrsgVRKxNSDbeImsnTPbe0B7cEjhAnnw6HBqHCSghcSF2Bbuax0QW5gK0EkCnQgOwGn4\nC8EafAjY4FgsT/jhG6+vsAihL7yu6g8Clabo3FEbC1riWk9a5jTzmp4TFCligU5EWjvTWASKeVVz\n/8FdLl28zGi41hGn9Mas70idmjZr2t7OdFFi/GR9oXQVNYbIzmSblro1saAoNUEI0izDI5hXURjW\nOouUkrap0EqQlRGy3jrHcmFifYzAbN5FLYDUCXmi4xSrbfjUC59ACc3D3fvAd59oKz5J9+GP4TGj\nfvb42R/z+wH420/07j/6bv/Cn/SWOhAjiYgEWzLMBNNFZLg5Pp6QasVivsAaS72s4zSg95jWRuSb\nD3jrO5Zdg0hbdF4glCQphiRZHt9J0BXnTsPAiF0XBFmgdQI6WdHIoRRpmhFUGkk6T0969bHKrGR+\nGAihJtdTnko3+NInXmG/fsSV7XVG6kVu371DmUX237a1NI2hbhyeBp0OEMkNZstHBDegMhbvPfPK\nIoTjq19/nQe7J1x79kWGaxlaytNcndMOBB2+IAjBXEomSnN7sM5+OaBd34a8iLMkssEsDKNixIb3\naKmoJweIfEEIhlRJghMsbUVqPOWgIB8NkJMpRlhwMPC+C7vjW8vuuqwKYmejPoieWUSjsHL8nVHo\n6dmq5YwH9+9hnaNYS5AVmDbeG99hPlQS24wqdbhljSw7ZMSKv7QHB8U0Cx8RgypN8d1g1Xw2Y7m+\nZDxaJ2Ik5Cp1WBX9RLfx+xsuVhWLD22a+H/eOZq6xfqAUhrvA7JrO0ZcnUPLqO8ZfMDhqb1HCkFZ\nZvEzdhfLd9fVOMeybXl4eMxwfcxAxeYqAk6mh1wcllx96QX47T/4l2+/7viIIBr7KOA0Knj8qp52\nCgSBJEnwwVLkOYV0LICjkxnzgwnWGfYPj7tFZ1ksagQiUniHQJFqtFKx+mzj3Hye5zStwQfLlRc/\nSZLkqA4mKgIoKXHEirAUip21MSARQZIUI2yzwMyPGZVDlCpBqQgoEaw0BNp6yezkgOGipCgFs7s3\nWdbX+Lkv/TyLkwW3vvaPuLyWMbKbzBc11BWz1uDrlmkNtRNYe4SSGiMSnGtZNAbbsQVJAScTx/29\nY977O/81X/qFn+Zf+5kvrjoBusu9fFeXsUJQC8G7ScZePua98TY2TVHpEFtP8AJGpaaWnsV0QREc\nd9/8MuujMX/9S3+ZYB0f7B7ywb07iHRAqi6zNxhQTTXnhyNUXbNtDE8bQ+4DiY/dht7Qys7rqtB/\nj9GC1po446A6OHroukYusiLZhu+//jpt2yC1wgaHX4XMMZ1UUlCohiQYCqPQWUrqFGiBSFpkImls\nrAd4byGoled3roUu5Zov5hwc7LNz/iJSyogXET1mJU6vRirGLv0VfRWyi3b79GfVHXI0TezzKJXE\nzy1j81YDg0STSMnTl6/TtIbbj+5ifKD1ncEIsfUbHKgsQXRYnHZR42qPag2L2ZxqWbE+KMhSzdt3\nPuAtIbh6PH3ivfiRMQrx6C5mH5YFPlR57MI2ESJ9dVZSCEtd19hlzWx6gg+B6XRG8I6nXziHuR0p\n49MkIdOajUFGmiQEJE1rOFk2ZOsDXGHxkyVtNSdZy5C9KrKPVd/IsdfFwgJwEaajCkUwiuANwZUI\nlSB1gpDRL/cFqen9+xzefZf0cIpazLk0CMjsbZL0Cplu2ForkbZhI9SIsGThG7aLhJPac1TNEaR4\nLximLcs2xXgRh6VCXNiu8/xSwd7eLv/kN3+Xg0nNF0REBZ7NhUHgBCyk5H2dc5wNWOgUtKZs9nEi\ngG3xrgRKnGkQwnHl0pidc88yHG8iAmTzORvbazzcO6QyU4z3zLxlRmDQbSLTUaXL0LdBBaIr4PjO\nT5/FTggZh4ykEOA7uFoIXdfBMpscdUrKcT34IJAdHZkSsYYySBSv7IwYlwnrGyWJkiyAtvWcHNfM\nFoa3906orOvUoUUHN+8uUFcPcM51DFNwSiYb0w7RdZP6e/zhNRx834rpyqoh1g8IkGQDBqNLLE52\nsWZCpiTnBzlllrA2yHnxygUWzRJbHTKpG04aFwltpESGEOUPAkgt8b0YV4hTticnM4bjAUvVQEdF\n6A2k+v/bmsL/P0eX555e3nDmX/oOEgSwNhZZWh/YW8y4884baBHw1Ryhcp4+NyRRgu1hzvOvPkWq\nM6wRJIlmfbxGkqS0TvLw4Ijbe4HaHvHocA8zs7jb73Dlxotsbu1gfUAIj1IiUpDLuEj39++T5Jqi\nHqKVQjYtab5OMliDKnQLO6ooW+uZHszZf/NNpu/c5JWPb1I8cwMxGoKrCW6GGlbojRJfe0QJm94z\ne3DMte1N8mc3uZJp/s9bezwIKQuTsGjnCJVgXNxYiNg1MUHgW0ua5hweVfz2b/7vfPHM1QxEYJEB\n3hSSN1XKd9deZe4HyIVD1wvqfEnwE5RfYpsp1UTyk596ntde+QSvvPYKSapJdU4QnuuvXseZloN7\nd3n7nff5X3/r/0agOX/xKkd791gsay5rBV6S4tEhRgR99ciHTj+ys7MK0Ko3CqrbgAHrDVJKZtMJ\nN99+p6M1AxD4CmSw5Frw+SvrvHRxjXPjnI1RjkoVaZHG92osIgiWVUPdGB6crHH3ZMk/e/+AWWNp\ngziDB4je3nnHsl5C6CjoVZSW9aH/eWxoeu9joU90XTIEXviV8ZPBr9CIQoBrKur2PbYSxV/5iZd4\n8ZlLXL94nizL0GmC957Z0TFfuDHi0dGEP3j9PXYXLbu1i0zbSYJpLMH29Qq1Mkx7h8fMFxXPP32Z\nRGdMpkcUacG5jeyJt+JHxyiIx7596Di9UfGmBITUSKEwreXapSsIb5DdOKzSkjRVDMYFw7KgSFNy\nnSO8Ii8GCCSNj0M4H+zeIbghuEe0raFZLFnOZ4itiys462P/BsgThfABs5jig2CQD+I4r498hc6Y\nKMpKTFGaRc3ywV04mlK+8BeQ56+AacCmhPkxoZ6gVcApSUJABcM4Edw9nPBsNmIym+O8pbWBKlhC\nUBjX4EWcq/BdMVZIhfWxBdsYR9rN0IczXy1x8vEtqXhXZ0g1Q+oca2d4v8QsHkKwJDKQCc/2+hq/\n8KWfZufiDoM8QyqN8AEnWjLh8dKzvTFGPHuDS5sl793d49gNEXKAp6GVkoaAF5HA1HdTVSpEujwV\nYtSiRIdXELH/Lohw5oDvohzJbDajaWI62Heigg9kieTCIONTlza4ul4yLhIGRYZIdUzllMTJKC6U\nIMhUpGjPleDuyYAPjiv2Fw3uNPJfXbfIReFO6waryEt0Lez+dzuv1bFnxycDUaRcQHBx2hZJ4xzX\nBhmffWqbv/JTr7CxuUaRjKNjlGDqOeX6kLCYk4bAja0xQsw4qufYxsVIVLgI40YgtF6l1r411G1L\ntayRBAblGGcss/0/Z1qSwJlUoWvhnVZxQJxOjiFA6QSlFbPjfVLb8MKzz+PrGaJdEhOPuJCklEgv\nUba7ScJjmzqOpDooNGR+wf3jE2QaSErN/GTO9OiQ5LkE6T0Gh5URTjrIcrzztA8esH79MvV8gs5K\nzP4uMh3SnLQ0RzXnP/ciwTqs98yO5tz+Z3+Ke+cOWzOJPH8OkgTqY8J8D//oXfxsij3ZxTWGYGuE\nbXFNjbEg2kAlalK3IDWSunU0skTIBC9cF111xc4IpGBRV7TWokW+AiWZEDAI9gR8H/jDZA1bbGGb\nlCAOoLmFDDWpCGgCVVvxq3/tl/j0q59gZzhCNy21CBjTEEILSHzbQHDgPBmBn/z8p2nnf8QP797G\nuZSxKPhTWXEjOEZSokIEO0EEOokQ6wy6A5ZpQvTGHeKvV2GSWuNcYH//UddK7taGi8SxX7h+jlcu\nbfDcVsEgTxiUOcP1daRS6GKAVNAuKlzTUs0XNFpj65ZUBn72+hr3tkp+681dKudxzq+K2d1CZFFX\nJFlK1+CIMGsvfiR9CKvH8fxiKzV2rCSBNCtoq4org5x//6c+wc9++nkuPXUdXZSIbBSXvlSYakZW\nLfBBUQwn/IVlxeaDlAfTJcFbKhmTLWfsY7W3KA0IxnruPzxkZ3uN9fEAGSTfuXX7ibfiR8coPFZZ\nDGe+R0y9FFHnIBAVmCWe45MTzpUp1eQQbxtS0Y2M+oh2UzIaBicFQcfXtCoBIbHes2zaiIhxjtYq\n2hpMu6RtTeT5VwrnRNcujaGsFJLUBpZ39oBAfVSRixEhBFxb45UnKST4gG0d08MJdTWleXTEM+e3\niZrvDnwFiyMIHiEVUgqct4TWEIyjSKGyjmY54eLGBW7vLcgdLEUktg2dhgArmLOInAESrI+dEqVE\nL0uymknYB95GUKkhSXoOX1cEcUhrKhLhSKUgSxU7wzEff+EZ1kclQkTUnK1bcI7g2xi2ekHwAmej\nstKFzTHXLm7xxgf7JEIjsiugAmb+Po31ZCGmLrIrE/R3/PSrc/8i1h3iSHM3mCQCdVND7xyIm1Nr\nyQsXRmwPNEmq0Knu6O4EQkZ+RhkiAbB3pxtYdulgqeBcoTg/zHg4r1l0DqQf34eAtea0NtAd/Wgz\nnHZW+mgh0JG3ALYDHAkhUKokVTX10vLSjR3WxiWqKJBZgUiL1R5QaQlBkg+GWGvYWhtxblGzkSfM\nGwsovDWoJMWZdvWep0AJaGzsXDnnOwj2k4vBfCSYl84ejy0SEY2BEBFFJog5qJYKhUAHB9bggsf5\nEMk0CKR5QpZlqESjtEKqJPa3rcMaizOGqmnYPT6h7WCiy9rTWEdrPE0TVai1VLELQWxhGuewwWMb\nz3KyIJMDCp0jnMAvDb5xLJeREmw5WbKYLjm5dw+/2KdoLesvjgAVV6d10LbdZw7IJEWVJTJLEWnk\nCRS25biZUKaWMhFk0pOoBBH8iqkpdMQbMvTGM7YhtdYMR+uPQZstgfvAu0Ih8hIpB3gRMGaOCo5C\nRM6IjTLn5z//ac5trZGnSTQ8wSMJHRmJ6pS74vh0/AyC8SDl2evXWNdd7u1gZmCOxHbFzd5A9dOS\nobvpvTvoiU2FjOnDapYgBJw1j7mNEKBIEy6OMtZSTZbEseEQPKatsNaTJjllNmRYFORJEiv4fSFb\nQKIkhRZcGecMtIqcBUp2E61d29GHaHD7NdoZrsenaOMMRD/tGnygdZZT/gQQQaGFIJeSS5tjkjRF\niIjljJqWLl5nJdB5hk40SZowGBZsjQoujDKGWkJwHVhLdAXtXqounp8PcThstlh2KU6MIJ/0+MhE\nCj0Si9ADUHqPIFchW9LpukkBWaHBNEgcw7UhqsgRLo7eKhk3npZxLFnIOG8evMe6OLpsjafIUjIF\n2loWc0NVtwQBdVWjlCKRCdK1zEWn9SgEQiicyzFtg5kZkiBJhUB6R1ApiYbDm0fM9+a4QU49qWlc\nyguvbDH40k9ANccfHBPu3UKMc3CK4DUyH6HTknRzE20mpBuKzXSO8XDzg9ts5ILtxiK1j+IwwcXC\nYuhKdCJBqxwtHaPRkKwcsTHKV0xIHkEFfBnJkU7Q+joLcwJ+Aq6mUJrgWj529RxfeO3jfOpjz5EP\nRvgkY9FGKLCUEkITe+jWRUUnKXEyISQKnY648czTjJIvs2yXVOYtWraZupYqBAofSLsGjiOCqPzq\n/nd4Chm7AaJHmQrwXuIRtMZEtKNKSWTJlY3Al166xjArqID5vCVNPOvjjGeuPs3a1jqJyCiSDMIA\nU81pb5eIxYLWCBazBUsCTjqujDJq55k+WpAM1lA0OBvwztC09WoqUfTRS4iAtiDESmP0scE5wSr1\nxQakUsxm+6xn8Nnr25TDIWqwFiczVYdGDLHjJdMEO58RpEIkOdYFyixjZ1yyO6u5uztDCbAhoJMc\n4+Zn9lFX6PSBqo4AKZ1K0vzPm2xcf/RF5S5n7L2BPC0tdGUGh3OCMk0Y5jlCxPFjYS3eOpCSNEmR\nWtLaFhda/KqzFc1pmmhSBWWeksrAslrig2C9SEgIuKZF5xFco1Ype3ygipR8oCP2oRVkeYKQEBKN\nLjWqzBBFQ1JmbF+6yvxwgS5tZA/+4Bb1D6f4o110IVDDGUEHpHyG5u5tbr33Fu+/fcx8cNThKRzH\nxjNKJEUiGAZJ7sC6CGhxPpCqFEJgZ/si1y9eZW2UkKWBrQvnCb/zj087DyIyK8kkozX3o/p1kqLc\nBrXdxZqWy5tjtJBIEbhz7xH7xxP2D09ouvbcKE34ic++TJ4pFos5k+mcR7MpzgnGScHGaEBZFthZ\ngw4S56vH6e+6XeM4pYQ789MVIvE0UhcrCHG/5XRWksmCZy8WbA8KDhaOw6pmb3aEdRaB5pd0yUvl\nBTb0AucUotFUzQXe37vDB/f3eHg4QQuw7ZJMSnKlGKcJITiwDWkKen1INZliTIt3lp7NZcWM1bXD\nQlcPO9uYXC3pEFdyAHQyRKkqqkxnJT4dIsbnwbYrEhRvDAGPTBSqGJCIDJENcMowN66rmHUYiDi3\njU6zLi3y2NZEXIcQEZVrPTKTp1iJJzg+QkbhdAhptUC6C+C7C9uPCBvr8UKRjDf4kzfe5Ac3b2GN\n5fntARfXB2xlKcvWcutwwlHVMq/aSHwRAttrJTsbI15+7gbDsuDq+R1Sk4HAfQAAIABJREFUlfDu\n/g95d2+Pjz9/gwuDgFkcs6wjC86yaTrm5YBDkG6ukeQZo80LzN9/gMGiM01bH7N27UWy9QElEucC\n6c4W+eA1jn9vwvibd6nrNzg83OXW925xYT3nhc9/Ensy591vfpX7h8c8mrYMRgOuDjdofMPB3LKR\nS7wPXFvLmTceaDlpHIcm4JRAs2RUlFzTDeNwwtPXXmQwXqPMM5yIw0g1gomAiRBYPFIHrIh1EJ1d\nQtpdnju3xic//iKDXHPn3iOUyJhVSz744DZVXSOV5b5SXFwXXLtynu9//y3u3j/koHEoobh6boON\nNKdMExIRxWKaYJkHzaNgSGEleiNC6KYyu3sPZwR4BAQZQ+TeINP16ZOcJIE88Ty7NSYHNna2eXlz\ng6AUCE3QmnpR8fa3v8PlnRF5eMDJfM60aXGi4NmXXubTScp0vmR6uMvh0RE3b91ms0jRElxbobIh\n0tYM1kZMT44Yj8asjcenRVAR6zYEsRqd/hB53GMFSC88UuUo1XB++xzv35ny3lffpG7mXDq/zef+\n4isUoxTpHPOTKXsPd/mnf/Qd3ts74srmOS7sbPOFz32R6qt/zNfuH5MoBd4QrIg1lI6AJ3iF8FGP\n04dAYwxpAKn/nLUkzxaaHi83nm1FxhQDQTctFtg7OOY7P3if+7MZzkNTLXhwkPGZGzsc1Y5v3T9i\nOluwluc4mZBIMJOK2WLJepmxc26bwXDM2mDEIE0oE8HFMiETDrOY0KgEmeSYpqFeGDw1IYmeqp0v\nWPolshwSrIFUMt65THFuA6kVepijWkswDrWeE8oMZ1qqm/c4cgv2FnOW7ZLn8xHZ+BrV176HHow5\nfHifQ+u4c2IwBBQe4RryRJNpCUGwlkbOgSbEqbrNQcKgTHn+8g6bF3aYTA3Lo0MuPn8ZSWREMkLQ\niOhBhFTgK5RMwBmUWJBqxXNXLnB//4j5SUNG4MGyYmkMz1w6R3U8g9SxtZmjE821T7zCV//4W+we\nTlh4zc7WkL29I8TG5opF23qPI8UIwwzPNFiyvnBMrCvYM/c7kqxE6rjuhnfRRfSKSipsW2OWhtZU\nbBbXGWaaQVny3sNH/NObB7x8fovnN0c8d2kbmXh82yKKBC8MiRJsJBkHBxPe2t1lWRty4VkblayV\nGXMX0EpiIxEnQipM2+BEesqI5eWKQ+40UghnVmlYnffpEdfxstrlwnpBpiW77+3y4Jvf4v5ixvtj\nxeVzA5762A2KtTH7b+/y5hs/YDKdMjy/xtIkfOWtm7y1P0E6uzI7PYoT2ZV9Orj0inongPOslM2f\n9PhIGAVEjxrsOfn6Hks/rdi5i66okqQaqQLXrl3lF7/wMg92D3m4f0S1bFkvEl545VUKrVHuq1Tz\nNUxrWRuNmJjAWqEoy4wL21skWpGkOaUQXNkYszMSXN8c06YaIyVJNgSlyQqJ0I75IiDRyK1zqKwg\nG6/Hy6/iXDtrI8SoRBcaHaA1FpVIcqkYfOYS5QtrFK/eYH26y/7/9D8gdEb58hcJt445WrRslU/z\n/OWSUIzIUsf+wS6JcpglrI9zEp2BF7RuTq4sSEiV4vJoCO0xl0vBpSsX2P70FzBSgBS8TYT7V0Df\nqfa2QViLk4IkLVgfb/P8YMlPfv4Vbt3b59KzWyRoJvcfsF1sEiYzXr1xmWGmufDUDq/+zF9iuLbO\nz/3czzL65uvsPpiyUZR8/Rtv8cbyPh975jJriWbiGioURmS8Jw1HZs4LvqUMgWFXLQ+iRzPGcDei\n9iAEGT2eiIU/LSUoRRBRmSrRGZu5YlgmfPkH7/LmfsUX/uK/wXs//G12j/bQpmLn/DphUeFdyv7B\nhGXrWZg5bTrkG7cfIEPBWx+8zecub/DaM5d4dLIg0xLTGOqmwdaBJMkIeGazCd5fjPqigtW8wtmI\n4MOLWkiF6KjR4gYWyCxh/fwFqvmEL/zar/Le+zcxu1PWLl5CoBBN4PU33+K737/Fr/6d/wSXpbz1\nPY352j8iH2fUs/t8+lMv8e0PbnF7GmX6ZNCPiQpBJChWKrI7SZmQpIMn3o4fDaNAlyMBpwSTYhVB\n9BNwfZlaCFgaw8Wh5uKVdU4Od9lZz2nLlAujIeuDMYlr+Ngzz1AvGibTOVPbsCkU5y+c58LFp9ku\nFN4sab3HScWwzMEFyiJD6uiJ40S26TASdOOpkOQZwTUk45LQWpyInIK6SNCpZllZ2tbifUB1EOns\n8gBZZqSyRYqcj3/qBbafegphA7Z+iDANx2aXTzz/U9x97zsMCsFwYw1vlySbGchAtYxFxlxLci0Z\nJYphqnl2tEWbXuIX/6PfQF24hpOaxjR4qXhLxBTMhmgcQgfG0SEjWEc2uMi4vMblrSmlkvzlX/wJ\nyvU1lFa8/M57HBztc3xSMkhLfv7zn8UkGbkUKB+4fOMZPitSvvbPv0FVN1y8OOTzT73Gej6k2puA\nOUaKAZKMJtEYXfBw9oD1DmijiIStiqjSHSn5+2hR0E89ChldYZllmHqJUAq6LkGmJD/3yae5Pluw\nrP+Ej185jxAt57fGpFJSmQZbR5BUTuDS1W0WTvLCs1c4mrb8W5df5fL2mGYWOSpSFXHDrXG01rFs\nHBujIcu27jZgF70KwYfxCasIN0A+LDDLJSueECHI0yGDVHF1Z521YohWgq3PvETiYfv8OVLtkCiq\nSYVEMPQNJGvc8G9QPnWOr873ufjsVQ7rNXJ9N8oNCt8NtASEEqhERXVuIbqCo8O7Ns54POHxkTEK\n/RU9bXBFSisZwmpx9P1e523ELCwmbI5KXn3+SpQVt5CplMvFgFllObc2wA9KLmxu4ING6IS0TEA0\nNMvICrj0gSYUXDh3EVcfY8oSkSasr29QOcNSKFKpI+EJJd4LBrlmPNjEzKdIqSJ2IW0Z5WMSFWht\nJP/wS0MyKGLP+txFEHNEeZkkqXj2r/96jIqMx8+OuXp+ndnkhLXRiKd//T/A3/s23i3wteFkusdi\nts/9+oDgPYl05DKwpgXDPOO9tuLGzg43v3ebwU6FuriDSiy107TEvL0hUHUIvOADqVOIIGmOvsX7\nR3/C33zuJ5nc3qd45TXK0Q1S3fCJz10hEGidocg0zdEevqkQbOKDpihzLl+7yt+4do12UeGtpV1U\n3PrO24zKgjsnR4jmNiooKrtJKwTHQjHGRU1GYEwcBsqILVPRtVQjEa3Ay8gPqZRgvLnJdD5jkGq0\niBUKKSRbKrBzbo0k15F8NUBjDScnk0hThsS3BgIUvmUjSfgbT28BDrNYUpuWH544FsayNshYWk9r\nPYg44N22FtM03ZQsqyEn34Et/KkZi0Al2T/yq+EvCSjhGKqEgTF8/LVPIbMhiAQk2JM9XBtii3lh\nUIlicu8W118sWHv+Mk8/t8NLL19l/+E+//DLX8c4swJxGdN0sxkJIo0zJ74bD7fOobWOhvQJj4+M\nUfhwxrPSw4v/18Pgu55xbElU0znOVPg2MMgkHshkQm09dePxIcGZOUFodFrg7RBTP6S1TTc6LZlZ\nQesDg8KTlGOuDwYcpyVTF6cgVTCEED2TD/GGl8MEnSU0oxQZAhqF1oJyOEBohVlUBAzBxj51pGwT\nHXXQEmyDMA0gCdWC9vAh1jqSIPjg27/L+tYGiR3jdYF1t5EevDORlNSHDvsfC3VGa6bZHHHtCvPg\nWDx8iKxbvIzFyR681M889B0eLwY09n2sCORCspjWLKuK5vgAqQYYsaDcuoSQkjR4fLtk/949dJrh\nOCJtG3QeMM2UUBvSfAtrG1pbYTE4aam8Z4OAwOFDTRA5XiraIFk4hwQG3b1fImjpI8YeBCTopjsi\nUCnLcD6wPsjZHo4w1hJIUVLFwltro64iniTRKCVwxE0iZScr6B1CpDHwtIIkgCEiRZetp5Aiqn13\nE7TQTWg6tyKroTtPVulDd2GhQzEGrLFIpRA9vkFAUy9oas1i3oAFkcSCb3ARno/3WBNYOIcVkt07\nuzzz/IsgkugM3Jg8bTmcLqIilwCdD1gu4gSkbw2hfXxDOR86bYkn34sfGaPwWPi1erLnyBOn1V4R\nCSucBzM6T3v0kDVpsI1hMl9ycvKQ2jccHE7wTjEa5VFZp32IVgJX1Thr8EEwa1vabEyagQ2W2p1w\nb/2zZOUG+IQ8AYzkwf0TkIpEKoIEb1qMFMgSkiSj0DmJlugkjYAYL2icAy1jRCME9c1j8vEx6Cnh\n+CHtB28ix+eo54YP3rvPyWLJ8njOoj5Efvl/5NXX/nWacMDk8A57uw84WVScGI/pCD8THRjmBW2S\n8Ov/+X/PZOJ447v/B8flASHk5GaMOhiwQew+WDq1JyFwCBRRSEQIiQmKb968x+efvcjx7m2O9++R\n5QXl4SFZXiC0Ytkueeud93j/zgNufvA+ukgxxuFFxd/+d/9Djt+9zeb6Bu2yZu/eIw5OZhAinyMB\nKrsgiCbiPIKnAs4TlaeS7p7nncKVQMQWL3RwY4FUmqwYI/AkeG6sJdStwTlLqgKDImW0vUmzqPAC\njk9mGONJsoLRxhZy7wSCIy9zNjfH+MZjG8PMWaxxTGrLrLU8nCypW3tKsCME1nsWdU2ftvcK0iuD\nIARCRJwHPsYNtq0hRMasTElSASSS949n/P7Xv8trLz9Nvr6OHAwhQF1ZhFagNZ4E4xxfe+Ntnnrx\nabbHL+K94s13v8F337nJg2nFtHHsXLnBUGf84N0frNq+UskVExTAsjXMlw0X/nxGCl1L5WyPWpzJ\nLWGVRshOd5CkwCU5pl6C9Lh0wO2Dh3zv5gMIlkxLnjq/ycWrV0i7uXUhQSiB94K8LMnKgixPoNxg\ncP4S9z64R27muGKIbwJ128ZRa6HQUhCE5OTkiHw4hJnA5Rl6XaMGpwKeWmkSoXEqagMEFyDNCSGH\n2W3c0T5iuIlzjtvfe5M3bj9iOZmwXNQkIZAWBc4+YLbcY29vl/l8wbK1tEHhe6UZKagtXHvhOTav\nXyWvG4q1v8b0+IivP/gquJRMpisthv4rIQ4nhdAy0GMqXxGERFpPu6hoTEM1XzAYj6mqhmK0RtAS\noRLOPfcxZiJh7iPi79HuPnmRUbUtVV0TDg+o5wvCdNppIkbmJYsg0zlL75gGT4snj/AkolQL5CGw\nxK8ihRUlf9eNEFJRFgOyLMcYj+7Ym3wIONPQVoHAMGI3nMW2FqUUxgWOTma0xpIkmrqqaYu8C/8j\n8U7TGurG4lxg0dpuYLdHLEaH5R8Tj2EVxcZi4ymzUo9W8N0E5Up/RMCFtTG7xyf84N4j6vkcnUfN\nTYRiOZl0AryONjiMsXidcnJSMZYLGjnl0cOHHMzmNC5GT8pZ8mKt3ypdJyJGMD0A0DtHvVziXR9C\n/KuPj4RREB96tOLiCQKk7Nh8idVcKUikR0iP1xnZxjma+Qm1D5BmXHn6Ka75jOPJHmu5YH1ckiQ2\nKucgkEkSZx2aFj3eRCQXWFQHfPpX/k0kGVsfaziZ3GE+OaatW9LWIE8mWOextkVIjTAGM5/QzCqk\ns5hijC5H7Lz0TBQxsQHlFDrtZjGsJxQFtp6gvUBsXIgV9XyNa75g/eJFvvI7v4eSOT/9wg7jrRFN\nfUBtWybWcjRf0thAo1KsJEq/IxitbbJ+6UUWtWHv3iGH9w7Y3Bjw8uBzFMMcrUu+H2KEMOnAQoMg\nSEOgxQANSikGWvLpj29xfm3AbN7SWs9yOqfMWqbLGXq8yc6LH6dM1ti88SxrX/kKdV3x4osvsn7l\nAov2mFnTcP/2bepFxdffucVR3VCEqDdpgMq2GKmpVMLC1cyAdSF4jW5TcSob10cHfdroiINfRZIy\nGo6Zz2f88a09djYK1mrJrJG0wMAc4UTOZDZlNl9irWd9fY1RXtCsr9PUNUcnc+ZVw+Z6ifSe+XzK\ndFahNExtS2Vc54TCqsode/4dqpMOM9GlOB2I4nSWo/uu0pxg40bUSiJCYO9kQhCCW8dT3nu4xxW7\nZOvCOWSSsXZuA+sEbWv5lV/+GaaTKfd2H6AKxb55j4cfPOT1uw+5f7LA2JhCbw8vUWZbwLdOrYIL\nfUWOgMA6z2w+Qz95R/KjYRRWxxnUGn1rMkRv28M3Y/igULITFhWKRWtxrqWZL1nOlsim5bxeY6RG\nLJYTTOMpC0kqFSFEFKBQCaKxLNtHqMGYXBW0s4ZslLCdbLI+GtP4QF0tSPVDqkWFMy02CNrWkXiB\n0nlUWhYa4T2+MchU4o09VUkhzm24xkEbIvWbC7j5FN9Y3LJBpSW//Gt/kw/+4P/h4aMTHu1WpBdK\nBuvrpLnC6pRlW2NV7CK01tF4eP/BHt/8vd+HCy/ifEKpBFlRYvce4snZvbtLS4jpQ7fGNwRMfaAx\nu1RCsMCi0MwXLVujEi8EJgScbxnpMs56BM/uvdvs/vABbWO4ees21ntkJhiNBjz19GWaeYUNAePi\njImEqEF56jsJwZMlOa1QVO6EhlNEozobDZ753jejAtH7BgJVXVO1sD9teHZrxNFyyUgHhDrP7HjB\nvDIoIdnZ3KA5WJCMU8y0wgWHFR4TWvJaogK01sZUL9dUbScPKHrEYL8eOy7J0BcU+23XPwdRrSuC\nmEIgKoQnAtlxQURZt5i61dZxOK3YHCeU8zk68ajUkQiNd1CojDYpSULC7q07CAF3Hx5wWDXUndix\nC5CZgtrMz+yXM4dgxU/knIsqVE94fCSMQh+GxWGS/jr3rDsS4WNuZoRDKYkSSeTAtwYTElpR8LVv\nfouL69soJKYJnIRd2LvNuNS0vmXj/JhgPNcubCKlZmYszi6wUvPU08/BtMK1Dcf7DyPz7WBIkipC\nOmDYbJMNDQ/v3iXLFId7+2ggHWyiVYIoC1wdOP5gH6k1hl52PKClQHoQDnybk403MA+POf7912GQ\nY8I2x3dhLt6mtZLvvLOPl57NXUk2HHJu/SJitMVyuc/J3GC1Y/94SevgUTAspjX/zX/7XyF0jlQZ\nOiu4cP0FQjVlcnzCL4WIIlREodg8QI0nsTW5AJdIvId/cmufz1nLZ8qcOkBwLcMAeVHSNlPq+T5l\nGWXtP/fCcxAki+Wc23uP2H7mk7z7p38Ebofl4hZVY6GjX1vS8Q/g8TgaHdAqJ28EKR6H6KjmO5r3\nFXNzrMuChBBoTcvJ8R6Hew9AJAgPX3n7LuvDnJ96ZpP9Zcvo4UkUqhWOg0eH+LmjlAUnkwWZynDV\nHGuWpGVGKqHMC/YOpsyd5O3dGe/tTcnyOK0YhYjb1UCWlLIDAYHsBvROC42BTklyZTiCh+AdSZJg\nnMd13IsOiQuOf/CH3+Bv/fJPotIFys+p78/iqLvVzI+n1KblB+/cRJWSa595icSf4+DbN8EHKuNZ\n2IBygXdnd1Z76KwWi1j90yOA/7wxL4XoBXoL2M8oeFh53CBjKy0ohYz6QVhrSVSK0YqPPfcM2zvX\nObhXc2HzGvODY+a7XydLEhIhCcuWpXX4xmFDw8IIvBSMxzlrScpycszhfJ+qbbFNhq0lDFOUDv8v\ndW8ac1uW3nf9nrXWHs78Dvd973zr1thd1YPbTbcTOy3j2E5QlDjGIh8MCgElShAIIRBSJIyEQJH5\ngjHiU8BRkDByFAS2gkUMMukYp+1up4fqqaqr6tbtGu587zuecU9r4MPa+5xT1XZ3WUpQeUv3vuc9\n797nnL3OWs96hv/z/5OPRmRZw+7BHlVRMjuJfQlhVpKYHDeoSFODbiwqTWmUj3kEPEbrqJrkMxan\nFWOpqN45Y/+nX0SlI5qTS+x+/CaLd79KefYqT+7exWqH+Ia6qplOl0y1Z17UzCuHpJqysnH3F3AK\nSmep6jlKN5jGsvjuLbRAU1Zrt7zbSLrF10/GpCbH2ROaAIuq4fbRghuXCvq9DJGEVVkjAdJ+StYf\n0hSCDg2zaUnZRK3Gyd4uZ49n+CalKR11WWJd7BGInIIbsdlIJa/wEhGW8yDUdBWSzv/tuJK3nEUi\na9H8/BznPEpHIrdZVXP74Smfe3ofYw3TszOGwyFaCVeuHuAweGvwtkF0wOSa8WiISTXL0rKqCs5L\nyyoE3nw8pWwsFofWyZo9a1uEVrXe6kbPUrZGVrZ+BlyzhBBoxGCMWochgShl/9rdx/zuy69x48IP\nM+xl7FzeQ9NDyDEGZqfn7A1SVmK59a3XebIUlpVFhcDKxdBgKZrp4nH7+bbWk2zje+ICWy7+hHE0\nhhCorSd2g8pamy/WeduUr1ZgonSIrmqs94iC0gX2r96gPpnym7/+6xSV58rBHiLw0vVrlLYgWIdS\nQukdx6s5pWlYFCmDfo/xaIJvCu698VUez2saM4rdf/ohZDlJosiS2G05HPXYU4bp3SnO1mgMNB69\ndxGDJxwckuU59LPoRgePTjTiAqZJqOvAPE0ZfeRj6MEcXM35b/wvnB494ZQ5kgyZHA45X8yZzgLF\nquLu8R2sKM5qy1lpqQOsguCU5oyKCotVKQGNCw3WWvxqRqIMpJPomm+NtWn/1SrBJkP29l9E6wMe\nnPxjvvrgDCvCS4djLk5StGsospz99JBhPyGkBukrdJiSN5Z+b0TjheLoTawvyILi1uPHUZKvXdVd\nJcwSXWeLxpmMpSgWCItgqWjVqaRLKsfqg1IxWSfBIc7y5OgRShmyJAGlKJqGP3jrAT/61A6Ho4ye\nSng0u81kZ4dB7xKjC1dIVU7ZzAnVCo9wenxCWaw4mRYsKsu7pwXfOl7w8GwZcTECeEvwdh3GqPbf\n9j7c4WbYem79qDUmCvDWYr2QJaads7HCclLU/OoXXsZ5y1/5qU/z7LWLOB8xOOiMdHfMRw8/zt2H\nj/jGt+8znnycS4d9Xr3zLVYukJuEV+5+lbJZvtctgI01ZcM98fj46AOvxw+HUaDTBmx707dALIpY\nwnJskjyuFQrpqUCJUAUYX77MJ196AbGeF1+8yslUc5GcNx/exmTgTCTLeOd8icoTBqkhz1JUAF+U\neJdRNY5FXdP4QNIf0FQ1/UwhmUcp4Wg65VxpJi6JYqwSe/exdRQZcQ2NE7wTahfRZCpE7gRNVPM5\nPq3oJRlJco66dJ39n/nz7Cym3LBDiqOHvPrNLxBUSTMvWVnHrHGcNZaVdSybmCyslKIOnqW3BLXJ\nWxBcVBeWyGuoq/P1xPUBnLTdiQGCyvEhoSgX9FJD6gNKwTuPpmhvaeoeuVGINgTn8NZz5XDMZNhn\n+NLHKerA2fyU8+mM79z6LtPlkvrY8WBVkrWgs071KbZwxb4L6yOiUouO4URoVavZsDy3zX8tpFjw\nnliXD6HlOWBdqm6C53hZobBk6Yg0O6RuFGliqauaRAZt6zw4G8VTytpxWtScLxu+fveIW6dLaFv3\n35PXgHaH32BmNoZgK9mx9ev2hh2T45vW6pYLB60VNgSsD3z+29/l4njMczdvkKU5JhvSm9SsplNO\n57co64aTcsm0fJfptKF2hthIPqFsjt7z5u+Xclz3RtBqfXzA40NhFKCLy8K6BLl2z7xGy5ZSsiiU\nScB5erkCB+eFZXBwSKUUJlO88u45R4+P2B0PKeqKC3kOpmBWNZRBMdIp42GCKIV3UeTFB8WsbKhS\nTekcpgavNK7yaBXIU0XSVkIS71FGYesmyq6FEPkU3Irc18ybBUGlFDWkOkH7VgDEGs7rkgsHOXpR\nkDx6FzLwZeDs7S8zffKIWVFSpvs8Ls6YrhoWTWDWBJbWU7fdeDVQh0AVPAofoa6qRX/GGlpLuBKo\npJVma8cwIcq+05wQMIjZxdUFfa/x2jIrHG+dLCiKAhUcFy9YdJ4xK0usM5zmJc9ccahkQOFXzCk5\nqxuOpksycgoHVhRBPKbN0HvAihB0qzoZAmkyJDRzSqAhUsVZNhM7asmq2AhlDHVdUFkbDUa7CyNE\ntazaUWbCw+MZg17KIDVoSfDJOWbYA19RNQ2N88xWFbPS8vqDUx7PCm6dLlnUtmVpcuskYwukXSeL\ntdKtt9CFDe9b/qFVpdwyDl3fTmQMC+vkpG+/Ry/Co/mKX//n3+Znfvpz7O73QfnIFVoVPD454d7p\njLuPz1k1SyTkLJsFihHeJ2zYKL7XIHShmFFCqjWj9E8aTiF08O1OZlxaMxdoQhUnQO3wOkKGB1mC\n9y2WH4tJepTecvGFT/Hk/jv8o//3ZcQIo+yEkRJGqSbRkBjN1YM9Rr0BPZ0iQShXK0CzzHaZFTWr\n5SnOWVQya62rZ9E3jPsp/SSSmlyXEZVtmGQp1WKJa85plOOm9xj6LPKG4nzFhewm1kUKM18ukJWj\nePs+n//SEj0447M/do2vf/53mZ5PqWZPmM1L3jhesLJwXDZxofhA5eNUjIsGFj5QEnBKI85jsKh2\n/Gh3WhAa72m0pvKgVEw2RsUoReNKEjlCVUuceIbDG6TFXRbMqVdwvwDvPL0H50y/9C32hwOuXr3A\n4nzBKM/weAyKqonkNKelY6hrdvdvcO/+LXZU1E5oQjRghQg2WMRbRDQZFiWKUmAB9ETIkjQmGon5\nI60iy5B1jvt336UTUjXGoJM+0tR433B0vuTZC5e4vNdnVdacL1a8c+8JSiX0+7dIjKZoHIuq5s0n\n50yrhkGaUolh2bjYhIVEFfFQxVq/d2v0Z5oYRv0BidGtsYrUgMRhZkMMtJVl6AB3gDEJLgTquiLN\nMpI0o64qFJ7aBW4dnfPn//Z/w+4g59ndPtd2+hRFzSuPTjkqGpZBkdJQVAUuBIIonL0fl04IJOku\noQVbBzcDBN326UR9Tk1iNjiaH3R8EC3J68CvApeIRv9XQgj/vYj8l8DfJNL+AfxCCOG32mv+M+Bv\nEL3C/yiE8P2laaTFJkikymrpVdq4tFUHcA6jhVTgQoBykHG8KiBJcNWS4C1ZCtl4wuTKAWkSKM6n\nKAWr2nI4zMmNYpil9BKN7oyOtUhjabSncTWVjX0VGksnc79cekJjqCTgVEqdavLJiIuTCXOjGPYN\nFw/32B9kNAj18h6EJcGvEBewVYVfrAjnK3o9IS1rxns9Ht8/4X/7nor7AAAgAElEQVR8+V3+9KUU\nd1YwXdUcV4GmCVRWodMxUFPbFUDLjQCnzsWeCyLUNaiwbnTa+MCqLfUpUJ7ER27E9ReuDKgM5xZ4\nnUIoGOYjsmIRYbdA3QQyhGHap2oCjx+e4X2ISME1yjRQ20BlPaWt2TGBvopiO45Y6bBAUAZUlEpX\nwUeEaNJj5QpmwFACU9fE+yH2E8S1GSjLgsViHsuWKvY7aBNb0RGobOw3GeUZg37Kwc6A88mY5bKk\nLGqWy4J52VDWDdcnPV5Ix7x7VvBk1SopIXGqdtKF3m/C9ABGhF6WoZWOIQ2sS5Fd7S8E3znxW36E\nYEyCSTKCLfE6iuc6Z0myyDmxWhaEAEVjWZ7PKKsaayNP5xIVadlswFUNtata4Z/FOhkrsqVFgW59\nh7CeA1maMhhdwST/YvkULPCfhhBeFpER8DUR+X/av/13IYRf2j5ZRF4Cfh74GHAF+Cci8kII4Y8s\nlIpEN6cjhuhgzbH8o9HKUHmoz6bokUaSBClA6YzQNKQEFsdzVrN3uH7t4/ztF25ydnJMNV9SrhYs\np1Oq5QKjNZNej0QUrrGRiFgHvFSYtIa6pPEpohU1dRx27yPKr9KMTEphV+TXdvDFCQ+qApMKTw12\nSENBGHh0o8mPNCd+wZ23fotJGDHoX+H1b9/mweMnfP7VVzkvZ/zEzetkruHP7vZ4eLzk7jzgvKan\nEnraUTjLqllSqR4lEZ/gTOSFcHaJdZF1WUtYU4Mp1Tm2gg+x2PfCxYusbGBWlGR4bFlGyjOdU1Dg\ngmdHB+riHrlR/PDBhMI5ZmVJEkBcQBMXg3EegidUnmAMg2GP2gfuzWcsGkemV8zrFK8VK4RMwEmP\n4BbU3gKG0MywpcN5hwUeEXU7CwRP4IJq1aBbER4X4Oz0iMVsSpak8azgcXW9pmmzjeetO4956dKQ\nLMsJSnN5Z0BysEuSRRB1VVVUZcXjo3POFxVfXkwp3RhpvUFjegQfayEd1LrjIDA6MkvHORm1J9cw\nFGhdg7Du9IVu7oLC45sSJJBoTWUtiai4GYlCKSHPMpZFgUaYlxU//kMv8vSFCa+8fp97JzN++61j\nSidokxKsg05mDok8wID3BcE3racd54AW2Nu/xiAdYusNZdsPOj6IluRD4GH7eC4irwFXv88lPwv8\nwxBCBbwtIreBHwG+9EddoERItML6tr4vgk4yXF2hTYJJe6S5o1xEcc1zr0iUiQKpRYmyFWlq6V96\nAd8UNHVCbjTZZIfJZEwzHnL68AHeeXTrKtKWQAOC8pZEK/I8RZcBoz06if0VXgQXYvtzbRucC5we\nn7Ccf5f9pz7FJJvw9tERBsfls11MLyPp9UhPJ5yfPOKkXHAy/ya33n3Ik+k5daIZ6TFfOzqlsjXL\nR5FopPQxD5DYmjTEpFpQAbRQumgsa1/hQ0VDq7TdaTqHqJr1nr7+1rjuXLmK8YrrYULINY9ufYOe\ns0jHAhw8wTWk7YRvvCPTikmeRcq3FskXJ3RNkgRyHZO0ed7H1Q6nSgb9lLIqaFz0VGoJDE2KU2Mk\nGaGqR3iTo6xFuxqFRxNDwHNAhaiRcNC6Cp2CMiIUqxWNrRGjUXoALuBDRppdpi7fZdRTXNofkkhc\nCF5aTk8Ty9feObwLUfSXiMsoGsFrs9YCVSYlOIV3q618QDycs5xOz5ien9IfDaMgi2mZVtaexqYI\n0BkUaZPAHeSpY1X2bfKyc/mts+28DDTeczlVfPSwzxu3DbM6qkOhk+jVuM2H0/LesmP8QjsOkoAS\nYTl/SP/gKXz4l9Q6LSI3gR8G/jlRjfo/FJG/BnyV6E2cEQ3GH2xddo/vb0SwPlA7EDFRAdg7XF1F\n9KENxIKWZmcwZlmueHB+j54/QFQfGwJuWZB6jZueolWgP7iE9IcUZYlylsQIRduTH5xD61gr9gGU\nFpZFxfnZOdb06A9TVPCkqaBC5ESYrTzWeoIyjIc5NwcZ6vDTPCym3Htwj3J5Qj/NKA8voJLLfOfu\nQ6aLGW/cu0thA+ergtoGTpdznI9AHIWmctC4VhhVDHWAhRUIkSNRHIhvCCGLCySiM0ikapNe8YsP\nwa8FStasO6203uRTn6BZFeyaHC+B3tk+p2dPCGh8U3Mp1TRNwyDLUCbS4ZvEMEwNooTaWeoQeRzE\nKGzWMEj77E32AE1JRekayqbGNjW7IZAbQ56ayGbsj8FciNCesERrheicarkgiDBDKIBZWxX5IdUi\nIENECIpvENcQnKVpPNoA5GjTpyjuoHXNYT5hrAXRijQ1mMSsafOj7l9ABYcQPZS7yxWFq5nXS7SO\nHqqrZ0ir7wFxnamWd9HahsXS8YUv/Q69LOOFF16k38tj+ZIN1fsaCdmmxLbTDj4ElNGt/mMb/4fY\nj5Mk0QCKDwTx/L0vvs3PThd8++Eprx0vsKhI/x9dj8gsrVOUTmPOpZlFsdpO/BTYP9hlMV2Q6UAx\nv89o+C+BZEVEhsCvA/9xCGEmIn8X+DtEE/V3gP8W+Ou8Ny3bHd9TDxGRvwX8LYDJZBzdsrRHUxfd\nGTFRVTckQRCxNCZllOWcF0+w8zPoJ2ht0FmP1XHB4cEYaWq0qCgEohsIMdHmcDFW9FG11weFI8ar\nlXf4bI8Lly7z0oufZGfUxytLUSwpypJFWVJXJYuyYJj3uPHoiMeLFSokDM0eOhvwxu1b+CplcmHA\n0dETlnXNbFVgMVjncSGQGhMbc7TCkKKUwbslLgQUFgMtldlW6VkcolIg8h+gcoKPmHqttktn7x1s\now3aKOiniHI4CyQ502rJPAQEi5GopkUQvIpxYp4N8TRUwWOUYINQNSuC1gxUioinn6URkIMQxNME\ni9YGZePutTMYo8iZr86oQo0STzAZRR0BPSrp4U0GwVPhqVovwdLufm1exDpLCJZerx8XqQIxQ2w1\na1vSLT0j7I1Sxv0sQqa7vIOKRtO3JVAf4r5cO4sSKJxnWZ63lO+BVL1/2m48gNCWUo0y1I3jnbff\norelTr45u8snyHs8jTZDhne+5WAIazKhEKCq/ZrOXpuUo2XF//bqE6qyonIxexCJfwRlBNtK2Hvf\noNo8Tdew1SU8T0+mZFmC6eVM9vcJi3/BClEikhANwq+FEH4j3kx4vPX3vwf8n+2v94DrW5dfAx68\n/zVDCL8C/ArAs889E0oXCMsVok1kXnYeLQptwtozcihENKPJs9hQYLThxniP11clzcgyPX/EjcPL\nVLbGhwQQqmpFXc4JNDShQkIPULFn3kEpgvQP+DN/8a9x8+nrHPYVNA1Ta7ECPrjYYxECRizGeu78\n3b/PY4EbeZ/zumLlNBevPcsXbn+X3YcLzmpYNY6zWuO8wwdN4zwqGbMojjGiqZ2NLd1t+dWt0XOh\njV9DO04NhCY+RoCExIzBlyRJhN16H6v83kc4roRWni3ATFtkmNBMG2ZnZyQ9w67qs5w2LLziR156\nmrFO+Gev38YrxdKWNLahaCw3D8eMVMZh0ieIkGpFP0nIUoUxJkqgLZaM0ozaByTvkQ96DC7mHB8X\nJJIysClmkOPDHlkzoXEeS8AuAq5agPYQGqo2AZkbcKLwYqJIvDccHh4w6vdYnM/wq8cIYEJJTwtX\n8pT9QUKWRfhw42p0tA5RAEUrEEVDaLkq4d3pChU8o9GIolxFKLpWbckwojFjBSFslrlSMQkeAmXd\nYG3L80BEY3Y5hy4X1hmGzrgrhE47o/MSICZSvQ9MruQsHtUopVg6T7GocS56Tx7AhVZY16/DPogq\n2aJUBFsJrGupAZqqYbEoGPbPW4/7gx0fpPogwN8HXgsh/PLW85fbfAPAzxHVyAB+E/gHIvLLxETj\n88CXv9971LXFO4+YBC+RoFNaNFscyLZ27GHZuoVGCWeP3yJ4oSFgEsP++Ca1CgQWVPM5tQu4psF5\n14JTTNuzrlAOgigG+4dceeHjXL5xmeGwR6ocSuC08VTO4azH+Sg7PhBYWUuhFR7H8WJOAJ6UC6zX\nLK3FlyXnlaVoLHUIUWTWOSoXqxs6SVAh0HhLMJqmLoHYjy9hA/DdVh+KEyuWa0OoScZXSHuKi3uO\nzNfgYLnynJ4u8K1L6oMnuC4zrSmLiuPZKbWLHsIgT6iD5Znr+4RmjntDaIKjWJVUtePChQnkQ6Ah\nyQz91JAZTZ4YEhOJS4rGkxoVO/CUIs8yPvbsITNfMy9XZEGRJgl1dUxvcsB0dYY3CnTCqnlEcM0a\n9ehRxEjRgopVFR8c1kZtjzQ11E2DMYbGWrI8Y5gaRr0kUtUrHTkNUHT8qmu6eKFd1IGirtnNEob7\nQx6fRYNgdExyChtpwq7/op3siLSeWdhCOHYIyBaFoGRzbodRYAv5GBPA279BR3OglSYbaHzdAvm8\nb6s7HpUAJhK2BOs3mITuR2z42Kp6tKGMUjS15dHDGcNe/v2W4HsO+cNJJ7dOEPkc8AXg27BGS/wC\n8G8CnyKO3TvAv9cZCRH5z4mhhCWGG//X93uP6089E/6TX/hFfFtGi4IuMQTQejP4WkeDoNqkWLdw\nmrrBekhMgkjAOottapQkaNVi7lu1nu5+i6piXlTsDQeEEEViQluS6mLBKC4a9QUlBOra4mj4pf/q\nlyAoAhGyGh+DSCu2rhIEjaioACRi2hAgqilLu/MIqsXYQxS9USCt5FkHf6P729YEb1+jI0mJ13Qu\nbGihtA6k4epPPkdpetQdyKHNPcTYM6wJczsyQZFuum7PgfZnN727bHs3DeO2uo6nu2NDmyNbj6L7\nLNBm/rudzaKp+S9u/BpBWZQXmtJSrRqOn1Qsy8D5wuF8oKhikjjSjMW+giQzjHZ6DPsZg0HOuJ9i\nRAjBMV+U3Hs45ei84N5pRd0EFqXdgL2UMMg0/Uxxoa/pGcgzw+DyPnZ+RqJBayFNNXiovcLX8F//\nxh28j6Is3TitjUHrbnTegrzf0NBVNTeEKNvPBwJZ3qeuim7A29eRdS5DpJNACJvvoRvo8N7vTGnD\n+Xz5tRDCZ/gBxwepPvze1lttH7/1fa75ReAXf9Brbw4BlbSeQfw9SMwUhzW4mZZqq6tjbyrMNjRY\nJDIjtYkcrzqwRnuNbCDUAF4bbBBcy9ftJE5irVQbi7Y7dYzoIi7IKLSPO0rovuWtoVmj4EJYJ3xY\n2+/O2TStRoBqF41ur+sSid15+n3XyubvQRFazycQd8j4/q6dj63BVH3mVkFqiA3UbbPR+oNuPBHZ\nmpibfIa8Jz5e32krs+7D5t5lsyLa3bF9/fZv610sxJ0tvp9fCwe7oAkqJbioSxnLkTa2NgOrtgJj\nXet2t+GRUcKibKVlKkvtAz2lkCSGYB5PXZU0PlB7abPzgRMfXfq1kKwLpDbSoVUtO9o4TymnkOhA\ns4TUxHmRGsERvcdtEpV4f61BaO9+PUKhm9vvMwJbBjZsXxMgVQleqih01J6qti5cv4zfNq58zyFI\n13L6gY4PB6IRCC7uWtsQkG1QKbx3YkFrhYPHzhryQR7jNgloUZRLSzrUrQGNbvyGZE9IvCZ1AUPk\n8RMXJ7qGtXvo24nbNLEnXgMWDaTtQtZ0Czh+Rg1Bgej4nCSbHX9r14j30hKbt2i4zb0FRFqdQOkM\nQXvPHYNHt1jjp433Tbcou7ArJTSBxgdU3URD0Y7BxviGdgG3PvzafQ7te3TnbBuE+BlD+1njKRsx\n2NZvpQt5kPYeu8u3J22bC4k6oQEfFN7W6ETRQ+FswHjBB8Vi1cSlo3W8BwlRljMEVGIi2aqPlYTa\nWg6yHj0dRWmtgbvW01hPEYSSjExPqIJjVs0IEtWzci04J6hU6KVgTo4Y4jl9Ysl7GqoQRYsJIC0T\nkxKkm1chbj7bO/b6dtsx65LIm9Ak/tdtdp3mRSDQeMEBoYVPhhDa8DpWnDocxZZt3nhzotAmwdax\nUrXmkvsAx4fCKHTxT5D10qDrYZcuVtrKDncDqEWw1iFZQjrIooxbO4OzYbw2LjJiVp+4KLpFkA1b\nRp0Q3cP47huRjdB6D5vJ3fH9dzjybjfv7He3w+toGKTb0VsvQDRBzPr5jWew8QI67r/41cS/xUnU\nuYcd+0BreNCEoNpwJBAVrbt0ZYA2cSuoltegHfD3fO4toyLbf+/+9v6trfU5wtZTgQiZ3DqHrVd5\nT1yxfmW1easuF+JAJ4q0xQKYRMhTz3AI03lLKaajkrRvE4RGK4zRrFaecaZxCL1UMVAB74WkNWQS\nIsTbyRiRnMCSWkctkVQJ1uVUEsglErVWi4IkM1TLQJ5H788RjQ6tt7O9IP9Qf3or4grbj99/ztYm\n3+lJWLtqH2/OFhSJ6dPYZdtA+N7x7t4jSXNMkuCaBmN6eF/wQY8PhVGAVrxUpF1Eslb+1Somy1Rr\nId+bZIm53K7BxJiYQVYiGKOjklRbAhJROBG8igOoklgijDXjNoYPnXRdiBl9F3ddH6KhTRQRSIKs\nOR9kK7yJRyD2BHa5AdPG2pvXjrqDHsGDmGhwVDQi61a6LYPR3sJmBnbNQpIABmnLlSIeCa5NNsb3\njMZJdxfFxbd29TdJq+45UbI2wBu2oc5ziI/X/2/nKIiK12yBjtoXbsdhs3WuvT0lGy+jNTSuEvBQ\nemFVCY2PnahGwNqW3YgILFM6zg0xgtGaPG0TjaLR/QnBW6al52RZ0XhF7aDXexY7X5GYAYP0KuX5\nN9BmwG7+LL20Ty+bYXXB48UbVLWjOLFInVAeNegk0rE7HEnKBiwWujmxDuRbD5B1mXCjEfE+y9Fd\nE3vaAMEkmkQpBpnGiF57BNYHisYiKpZwa79psOro4+KmFkuy1lkg4GzxHsPyg44PiVFoE28SLfk2\n0410ylFAx4KzCV8VeZoyJ7IExzkvOBuBNPVyhncO5zzBxzZg08vb5F6U0hKdtMPV7WxrRy5aaa8w\nWuNUbC1WLr5vZ51bE8PGWm+u33jTaiOxznvDBcEh4tFrG9AZmTZpGeLjgCFI0i6q6GVIMOsQRoJi\n7aSvOQ79xsvp6JHpXP2t87bisrW3FFj3IXRlML/uwtz63ujGgzY26N6vO2XLKGzH3usxb8duHQ8r\nQlC4oPCdCnMIEVm5FRfH2nxAnCc1EfJtVByHVCfo/pgQLKqx+FBSNCuSROGL+6TmEOcTgtSMkgHa\nHGJ8SlMsUanGJBmWMUV9igj0J6CTlKLyKA06EXzj1xFYlzDsQoG1d9rmG7qqBIENDLkdw8xojFJk\niSE1mjzVHByM6fczrkxi+3qeGQRhVXtOpgVf+s47kVIAH73jraR7N8ae0LI6+zY/98GPD4dRENaL\nXW0tfLX+vVsvm8RXVzoiwMGFUUxM1RVNXfLk7l2mx6es5icE53HO4WoPwbBzZZ+8P2RycECaZyRp\nH1G63YRDuwi7mr/G+UCSaHTrLmjl28W9NczSufi0sVvLJdR+fqVifKfEYNr7iYLrHhUcWimMjqVQ\nJ7QhhEHaBVI3jsY7bEgJEvMWgmkrGa3xQEWGa8Cj6fZ1UaqNSTc79fqxEHfabhG3YxrDnU1jboSD\ns+5SpHtH6Tb8VksxdMY6tI/fuytuByTv2TBlY458iOGR9xrrhcYFXMerKdEu+PV7tYCftkkq1Skm\nU4xHA1QT8w+9yS77Vz3qdA5FjWoKlK6weoi4hp38AkrtobRHBUVJSdZU9NIDtNc0bsmZdyQYekoR\nXEMfG0OlLofwhxxdCNBhFtbznJg3GGaGcZbysacuMRj02NkdY4yi10vpjTKSLGGYCKmGREd5QDBU\ndWAy6PHad+/xjbsnayGa7j2B2NviPd7a9vn3jfcPOD4URkEgIuTaioMSQev2y1Yb8ouurVp1FYJ2\n0lWLGUf37/Hmt74JtiHNG3q5IdGOkhKfCv1eDr7h/OgO59NTnE/RwfPCpz7D3sVLjPcO8UGhdMx/\naZMgzqN67YIJIbZUe49SWUz00IYnXXIJB+IRpTEmJc9HpCYjM4ZLuwfsDftc2B1xNn3IfPkAZyu8\nr0hUYJwlpBK4MexFzoOsx8oLq9pyslKsmoQ3TipWvk8TLhJIgCwu/q67TzJcaNDMwZ2y20spk+vR\n+/CtvVKdEQhrY9wZ3i56tSHyXQUf+RoiW1MA10RUqDLRpEkMbWxrULQodGiJdlszuQbyhPfNynV4\nEcdP0HGzVSlJ2kOpHsqc44qCxkODtOXpWDXQWsjyJOITlEaZhNHhIakGrzO+cfsRaZbyzEtP8dxH\nR1z96Md44/Z9fufzX6UqH1Ms70ftSu9oxEfDlgjHLYFOLrA3Skh7PSaTT9E0BXnSZ6A0VXYXe/r2\nxlPo7ql9IhrQWBoe9CcUxZS01cj87LOX+PQnbvLMCzdJtDBbFkync5RzUSNDPOX8jKbQnPoarTT4\nkov7B+yMx4zHGT/zuRf4Cz/2Ar/zlTf57S+/ztsnq4jxCJvchW2adft367x84ONDYRQ2noKsKbFU\n695vewpKOjwAbZwfUOL5zstf4+jhA/KkIcthbzwiM4osSai9xQaLkoxVaXFimagLzOcrxDnuv/ka\nq/MzXvjMGG0yRDTexjhVaYXoGIfHxH3EK9DhBLoSYAwiiW6yxpiMJMmY5AN2BxOu7O7xQ89eA1Vy\n+/53EGYofz+yMqkSweKVQUhxZsJuukOanLJrNI4RV2tYlpbaWR6tLMfVEBi1HgPryej9giSHK89d\nZLc45KlD+KdKx6Rm8O1nbY2s2uQU1o07IRoE52O40A8e5RpkcYwrFlSLU7yzqGSATvskO5dAZ5D0\n8BLDK0+bDem+086z63azOEqtZxaNe9xto2ETpVGSomxGL01wSUU/0wRnolJWEKomIFqTjccxbMz6\nJFkP39vj+MljvvP6GyAJ496Aqy99hKD7pOk5T3/0Arffucqjd+6QB0/R2FbBHIwCW3uOVzXBxDD0\nzqki75XcrL9Kf9An7x1QJoHZ/BGJf39uYPtHNAzapOQ6oURIlHAw7PFv/LnPsHu4z2o1Z962Sq8W\nS6w0DNo8VDFfEhCyHBqn2BlnrM5mkR1rOETnKYlYPvvSdQiB/+m3vw4hdpQG34UNrmVBlzXu4YMe\nHwqjIMQuSWk9BRHaRGNMXkm78JTapLoUgXe+8xovf/krhNQyHmZcv36VVAeSNLL+9LKMebEglDXj\ngz5qUZLYHJER3/3Gm2AMy9WU+dszFrNjnnrh41x59kXyXkLTWJyPVFomMRACXkUeyahNoMFHzsgY\n1we0TlBK0c9SLowP+dkf/7McThRKV6zqU05OH+HrByhXkaFw4gk+IVMZfZ/ig+N4Nic76HNjsh97\n7tMk1hms5YVLDZqEW48Lvn3meeXYEHyf4GLIIzpD54bp/IjghMtJjdGXox4jUd0KXBsqBLqaegCs\nc1hn6YtDVyv82UNOXv0dXLWgKeZ459eL3beueywRJgwvv0h6+Bzh2qdwSYbK1SZskk3JNEYu0rrd\nnSFtOzxpyU29IziHaKGpPdZ7siwhyXOGlwZgBqj+ASQ95ssFi7OKVZHTrxq+8vJrfPfeA1xV8Mln\nbjIa9/nV//2LgGcyTJn0FTeev4mvS47fvU8/izT0ooUkje3hl32kw6/rwCu3S57MSqYnlpBMGU6e\nkCqPLwy13875x2ONRw2BJOuRpgNOp0f0MsNf/dd+hE+++DSXDgY4W5ImCZPRAdP5nLIqWc4ti6ak\nKKuo3aCFuhAMwrTypEZjghDqBjcZYhLDeKT53Kev8+DJOb/36h2mlaPpjK/vksgdYewHPz4URgE2\nOQXel0fY7MRbMaq31OWKV195mSqUXN8dMchTEq3QSYTeinekicZ4g5KMVCX0M8vsZIoKsHNlxPGd\nEwKKZfEIObU0b3yLK08/hzIZrvJIYlqg32ZCy+ZDbZJ30hLKmgSjNIc7V/n4My9y/WJOmpSU5Yq6\nOqEuThBXILbBhRrrLeKi/qEO0EszlqJZolHDPsHVNKFB5TmNsmRpD7dq+NFrDc/vGo5XK56sPHUY\ntuGBxy2mVDPLXBbYxQH8dFcN6OL8SLogKsTybOjMrCfTAkfvMr33OtXxfaqzh3HvcjbWeUIksDVa\ncK5Bgia4hvndr2NO3ubi4TVMfhEfDJjoZamwjTbZxNbbWfhYio5gZ2cbarUiS4TVqqApm1iRShPQ\nfZTJaUpwxYLHd084OauYFQUimlXdUNY1iQv0B0MezUpO5yvGgz6PT87ZH2Y0zdssTqZcf3aPk9M5\n/nxBmkAQx6oODHIdw5I04dJhnwenFffvRw1P7WF3FHUos0zxtXfpkgSbidwuwCzN6cKnvX7GjWuH\n9PuRqUoZQwhCXZYcnc55fLSkcg2r+QyjdCTdbcD6mB3qNw15akh6hkYCTkWDvFgumewN+fRHrvDg\nyTmv3D+lIURvji7H0CVw/4R5CggRSaiIngKynrybbHa3ID3HD+7wnZe/wqTvSPKESRZItYfqHF8F\nJIFhPyepVwzrBheEYrlgnPapcs29o8d4m3HhwpB7D2akesJ0MWW6KHjjtW/y/Ec/yWDcIxCFQkOr\nMKy1b4EzZt2P4UNo23QNw/4ugyzl53/yJxjmFqkfUDdLsAXM79JvlqhmiVuWlE2F94ErO0NOzmd8\n5GOfYHb/IVd6KbPEI72ET/74X+TO7/82y1DT7/UpygqVZjyc3mV375D/4IcM33oE/+tbAa+GYAMh\npHiGiBzwcF6wq2PMDbS0Vm34IDGNaF0UWBlXx8xuv8yjV34PfMyVpMMxGkWu+xAcQTyiLEEF6sUK\nnWhMKszOFlSzE+7/1i/Tv3CTg5/8G1iXI3kPnUjbDCREcpAuUx7DlxBgTbgYumqOR/uaXClMr0fI\nBjg9QI1usjgreHK64Ml0xnxWISayOgsVZW3Z7+9gveX55ybsqMt8/a13+fprb1JWltNT4fw8pZ+A\nkYpQw2SY4yR+t708NiiJdSR4gg/c3DU8f6XfhrIKV1vSRFGXDbzMe3bhrq8hMSlGGWazY168ssvn\nPvMCk8TR6/VwrsZaT6KEYllBWfPc1R10CCh/AVGB4XjA/mEumfoAACAASURBVL7i6Fzz9hv3Wa2W\nVI3lycmSQa9BaU2WJOzt7+JC4NqB4i/92Eewv3+Ll+8cEwLoNKOpyw6T9kcmRP+w40NhFLq1r2i1\nHoTNPrIVDzkEHQL33rlNXZwz6Cfsmsj3J86SNIYsTRiZlJxApiDNEnp4npwvOLYL6saTm5zpvMQp\nQ9bPWC0CwQleHG+99h32Dy5yePnG2j9USsC35VIlCC22oP3EShm01mRGs9fPmWQW38xYNaeo5h10\nOmKcB+ZVwxvzh9QrzbVhjmsso3GPC9cukPcVbrdPKBoOB4YnJzPOjh4zuXad3uI+Z9MCbRRjk4N+\nDlUUnLkj9oY7JMZTubwtgVlA471CVLLGfcQ8Qjve0kXzEinalHD++peZvvNtgnebqk9dgAje17FF\nvY0dfNOQqoA4i1RC2jYbBR+oTu5jVqf4wWEkN0mSLcJT1hWj9Ze+rt/H36XdBJomeidaCXmeke4e\ncu9sSmENp/MVy8pRuBqsZ2SiNNvV8QglQ2ZFw0evfZwdgYNLPQYy4+XXHzBb1dx5XCAKatfnwkhR\n+4BJWth8ANPyMDoR0jTusq6yMZEnQnCRwi/Vf0iZr7svEcpyilbC9cMJ1y+MyFJYLc9jydxZtHhm\n03NuXD0kMbCzM6apYs+OC5APetzMFGOVcPvNt5kXBfOmYVl6BmWGrW1MEmuFSTW7w5QXru/x9TvH\n7Tir9Rr644QO8CExChAz19ukrV1du24cSRJLLKsnT1iczzh+/Da9FJbzM0Z5wjDJuTQZ8eyVQ/I8\nZ9DvRWYcidqTdVlwOjhnuljwjdtvk1QBguP4/uskg6dQVCgdEA8P79/h9z//f/Ov//xfJ6iEpoLe\nQLULKrbfRSlzISoZRKKWJE0Z5xWfev5p/MSRjob4J3fI2Ofe/Vv8ky9/h9miYLA7ZidLeeP4CXuZ\nYB7CS4Nr+HnDk3fv8JGnr6CV5Wf+/b/J+eMVq/s9dHOfpy9d4q137yF5j5vjiuOHwqL+Brs7V3lx\n/yPcOglM1R7iQkvzHoFT0QK0dPldCAFtzT+QK0V9/xWOXv1imygMpFqYZIZxnpEYIVWQKiEzmsQo\nMpWRiGJvMmA8zNnppxSN49UHZ7z28IyTL/4aFz/7lzGXn6ciIyjdIkHbpJcPa2r6FppATHh4rA1t\nD4MwLyIhrR9oltMppyeBh8cFdx5PEQLa1+zvDPgr/+pnuXTlKgc3nkJnGb0LNzl//ct457hQLnju\nL/0E/9bPLKkbxze++Tq/97W3efndc+ZFj0/fzFEStUkRIc1M/K5bgEHMa0VjqIPgvacOvmWe3Bxd\nOdYHCMHhnWeUGn70xWtcHKd4AtViAUG10gE9nr5yhYPJLjrRJEpQfYUxKbVbYZuaxtccTkaYp65R\nFCXvPHrMrKxYzVeELCFJFE5ramvopYaP39jhH30JSgdGNLbFROj3hWs/6PiQGIVNTqED93RJBaXj\nTi1KMdjb5eT0COMavJeW5y7jEzcucrg3YW9nhDEJSZqiTNIiAT0+02gsmQSu7e5w9/iMU+9Ruo9r\nykgH7i3WWUQJxWJKU0faM61aEFLXoyAbKHL82QGvhL3DyzzzieeYXDVQeWpleHx0l5P5MZNM+OjN\nG6ymcUJNzJiqqgh5YH5m+MxPXeXknYcMBwmPlgX9dMzB8znV5Rt86//4AwJCMMKsnvE4jBnsBMKD\nhNKes9+rGeeGc6tjRaRN1kb9jM5LaNWb2+nRNUZJteL87W8jKmpIXh33uDZK2R/2uTTukSeacS+n\nnyaM+xmpMfTzPlmasndhj15uKOYnzM6njNOES6Me//iVeyzf/goXLl6hCWMkxDJtXDHfOznbalp8\nHFoaPBel27xEkVTBczorOJquIq0enhduXOSHP/48n/7cnyGb7GJ2LsY9RadM9sa41SJKpvVS6nJG\nrWo+/amnONgx3Pqfv0pd1mgzIBEF1CitIlCKFpmpWo1LCWS9hCQfI0GwCRhVAo+/517itI0ozDzV\njHsawVPXkTW6Li11XXNlf59RnpNoE8u1TuHbJCfOIJUjFB7lPJlWhDRhdzRAa8W8jmrZSoQkMTQu\nWqRxntLTCus9RVVgtMJZt83g9oGOD4VRiB6X2lp0ss5SZ5laJ/nOzo54fP8WxWpJ01R8+vpFPnb9\ngI9d3aOX52S9MSiNpCmSZCAqsu40NdJzZEHz3P6SsVIUqwcU6YCFdehUoVYRMeecZ7WynJyecOX6\nTUQlkSM3xAy5CbE7MR5dhlejdMLh3gC/PIUkI1EZ7969w8TWPL93k6v5AbNlyRP1BF/V5FrY2R3y\nqCw5rm9zbXKNg7/6Q3zzt9/lEx99nn/2D/4Hfurn/m0m+RlJnnP/7dscXrqEc4437r7JpQvPUoSK\nolryFy5b+qrktKxZuRikh5bF2Rgdy7xeIuAmAhAQEZxKWd55leLRLXqZ4cqkz7/7pz7Cfi9h1EvZ\nHQ1IlDC5eECic3r9fXTQGPbxoUQaR708YqGXDPsQhg2HyjB9NnD75CGrV/8p6Wd+Hte1xLd8BBt3\nRdYsS13oUDexQ1Ipg2iDeIkyddbyrdvHlGj2jOLFq3v8Oz/359i/dEh/MkGMiey2aUIoVqhgITT4\nUBPqAuYNVDXNYsluYvjLf/oZvvjqfQRDaiA1kdtRt55B07Ektd7MYlngziN/Y+EtSdIZ13XEQFdh\n8a4hN4pru0OMrzl5siDJ0ugpecPupM9IhNQHQl2DaLxEHYeghSTpY3p5pIaXirqu8S4wyXMShCBL\n6hBYFRVJiC3kVVWTIlzf7XPraE7lHHbbAP8xYgj1g0/5/+EQoWsgknUDkbQ4+g0E+vzkmHJxjrcN\nvqm4sTviQi9Dt4xMOsnRSR4VpXWsvyslKB17KYwxjHo5F0Z9Dgd5JOjwAbzGOteyIccvez47gxDl\n5rpEaKyjb7Lp0uUVWoIUUzY8ePNNeLLi5O5DdmnY66VIVdAUJbZcMTKKREE/gaJp2BlkNEXJm6dH\nDDPLpad3cUXD/Pgur37t19HhlMPJS4jPWMyXLKZzxoNDXFHSy3OcX3LuV+z2hdwElI6t1KJih6ZS\nsWdEtRRlUSQ1hhRog52egG3Yz1OengzjbpMYMq1ItYqEtv2bGPqoZkQIY4JLQBK87+ESCzqGacYo\ntHiu7Y7YGfbx9ZKOfTka9i3YumyFiVs/XUs1lKlIlhIcrKrA0VnJo5OSW4/m7Ixynrm0z2hnjDEm\nhkIuEJqSUNSE1ZxgK3wxwxULXLmMnp+taVxNWdQMcsPFCyOchyAKLXqtPtWhJ7WSFrMRUFpiV6b2\nDPtCln9v5T+GaDFUS7TQSxTeOrTRFMuCalUwTDWTxET0qbORVKbtp1HSEsV4wTcB5xS+UZHbUcXc\nT5YYUtEYFHXV0FQNwUe8hRc4GKYkrVvgwx/DEmwdHwpPAejQLutOybWRE9ASKFcL7r35LVbTM2xd\nc31nzM2DCZNhH1Fp1EjwEEIMAbDdzhhJTRWC0YrRYECWGJ6drSi95u1H7+Ktp6cTKh+pwQTN2999\nnaeeusloJ0fpqIRsdOQgFmVQASKRSdspKYZqVXI2fRV9z3K6WmHcCadFzDKXZUE/MSyCYufSLlXd\n4FYVRnsuDse89rtv8ZoWfuxPXeV4cYJU5/zmr73MF7/wDT77zA2e+bF/hfnZkumTktM3v8JcGQ7U\nVZLhgNqfc3X/kN27BWdNLwJXJBKMrFGgAmt1EAl4Z9BBIa4iSwwXh/naM4hybQF0NMjF2R201Cjd\ntmDLgFBbJOzgFkt8HTszE1H0soR9XbOXCEfljCRRBBv5DqRd6JuGrJhb6IwrKKxt+wW0YlpEeO9r\njxa88bjApyOezpc8f3GH/VGKnT4mqBIv07bMmkX5BmehngMWXxf4usLWTeRrrDzN3NHMC3IVyLIE\npaNMqSiHdxZpuTe6Dl3rXZyXihakERu0ZGvuxspkWCcXjAi5UZyezTldzhn1e3jvePriRQapJriA\nCxYbGpQRgo29OdJYvIHgHFVZ0tQryv+PujeLsS077/t+a609n7nGW3fu7tsTu9lkS02Rikw7JKUE\nlu1YihNLsBwkQgADeY0BI2/Og1/ykgSBDQcCnNhxgii2bEmWYsm2bCWiSEoiKTbZ83Rv36nmqjPv\naU15WLuqaURRmgAN0Bto3LrVp+qce87e3/6+//cf1g1WBz1KJAS9OMY1Leu2xkhPlMVIgmP19XHO\n24liZkz4N3Qv8d86nsKlDOcC4aUL6Oz+RU1TcXL0GFev0bpFCUE/T4lVJ2ySEa6tsd4io6jzuHLB\n0tu5EG5y6Y4jUQjyJGa7yDDWEiUSGTtowl3NOWjqkrZpLn8mkJOCyWkA8IJ2IUirFVLGLKoTosTh\nqhJhp2htaGrNqjLECIQL7lFUYaTx1qBUiHvrK4lwgkeHGld5hnLA09de4vTwQ/7gvGLjvfvc/vzn\nSYuKRnvqtuTZZ57A7hvK6oB8cpvN3pCHNbROEVyZTLgIv1shfbke7DAbYYkkxJGkLKtgLW/D6tW1\nLVYqnDxFqhRdLRBCYdw8vJ/tQbAO90GlJwVB3CMEkZBIlYQCLS7sxTpFaveZX+zRfTdGdOnuWAfa\neirjWRvL+aJmWWqgpY08ZduQeahX5+R9gdcpUZyjhIM0Q/gIbAOixnmB7az3ZCSQcYRKEgSKXEiU\nEeT9DGHKwPp0vkuB6ohyXadwIb2WAtazhrwffTQ6fPfJ3BULJQRFJFmvGzyOumoDPkWggdMBri6E\nqOKEu9i9gYhDcRPhBhQ66NDJCBOGMdntcq22aG3C77aeOFKkXRjE9wAj/GvHD0RREEJAFAUYwV9M\nl90a0llMazk6OgxFodHEqWLc74FKsFGOi0IAiY8inIhI8glmeR4GRYIc2XqJ8RItJCpOSJKIUSq4\nsz3iwXyNl31idSG6sbTViv2TfYajCfFgTLB58zgpkTLp5MkOKSJk3CNKE5xo2B49ybh1lFNP01im\nqxJtYCNWaCWJkwgZKXQiGBQb2LMpKQ1+VeOjiOP3jhht7mKLmp7xLF1GuZzzxulj3jpZcTXP2Oxn\nZFhe++A+V/oJO70b9P2Sn7w5ZuUlr50qvI+IHHiluHhjw0kSTnrpJMo7rKuwccR8XvHC5hDVKftC\n4fHkRUI03CaOI7JhANps1YAzyJ7FmZa4NazWEsqQgCyzjChes55PyRw44zFd8RPI7xrCP8IXAn+h\no1zL0DGstedoUfPwvEa3jl5uyJzlRr/g5o1Nhv0hfgXx7tN46zk8uMfJw3vEeY5vVph2hVifEysJ\n2qOF59HDM0rteDRrsTJhebYmzyKSuLPKEwJrHF54ahPacm3B2gtmYKBY68ZdnqP4Swis89mEXiyJ\nCKHG1ktKb7puQVBbyXq9xgvY2e6BbomEC/iVSnFNgtUJPouxPsLEDY2FBgUydL79Qc7yTOOsZVWW\n9NMYrCfPIrYHGe8vmo5d+b2BjPADUhQu3lUhPjLuuMgzkEohpKSp205eIGiFJEkSVktDXZ1jtafX\nS+l7iGKLnwdU2NUabaFuW87nM3SriYQh9xYvJFEc08sikoVnbQ1RlBP3LO2ywbiaDz98k1ylvPjy\nZ3HuoxWUEJ2Po7jwWYzweDZ6PV7a2+S4vAcmqDKdlRhtaPAYrTkvG4SKGA4zNgYJWZYgs4R2VaG9\n43jecP/sIf2iz/YwYT5fM04jpOzx+GBOma7p3dhm9/Yeh/cPmUlLkqf0U8OWanlhO+eNaYvR4Ly+\npI1ftOvi8vULpNFMEofpZfRsQ+I9zkNLuF0vViVUmoGWZCriKgm9PKWX9PDNCu9DzuVZa5iVmvPa\n0jSadeuJlGI82sQYG2ZjFe52302tvvSJuOQwCLwP3Zi2jvmqYVpa2rpluL3NdqLInUZ4yJOEbDJG\n9neoa83D++/xP/zPv4VeTUkSi3INWaK404u4sbdJFgOTTT44mLE/K/nGoznaOF55eosYgTO2o7F7\nmtYFmrEJCeKLxncbXI8SkGUSoU23Nvff1TGEry54IHXTEqsk5Ehqh4pS7n/4IXk+4GRa0zQNG8Wb\nDMdjnnj2RZQTSAOxWGEtnJ0ccXR6xuOjMxprSJWnEI4sBl+GG1jd1tQ2IoskyoUxKEsvnMA64dv3\nePxAFIVwc+guNBe48FIJIhVhfMhH1MaAsZTacXOYI6zntTf22dnNufv4BCXh6eu7bG1vMOhlCASL\n1Yqj2ZrH0xnL2QIFpIMhufREPkRs9bOUfpoyLwMKjJMkaULdlBgdwlBXyxJpIe4Ho9TQhYexwQsV\n9vu+4em9mySixAvY7g/58PiAVVUTqwjpBEUSM08k07KiPFtRrSteujJimGZs3HyGtz94CzNtaRqD\n8CXolo3BgHEi6FvDq8cVCMnZ6Yy038e3BufXVL0NEiW4nno+tRfx6/cNa6/AuGBfJrurzwmCv2Iw\nIondlL/87C1+960FxiWMBxlGeIzVLNcNlRXEWc69ow+QPmH70T5PXt/juVtXiYB1U3K+rvi13/5n\nrOZrislVFvNjiuEO1nmKwRZr44I2w9OtROlwiY84KR9dUoLW0QXmeMrWUa1bpPCkRcyV8YDcaNqy\nJYpTZLGBs4I3v/0Ob7z1HneuDzg8qmiqFcena9AtZ7HieFlyc5CzeO+Ir717zOFKs7KeWEFZ1cQq\nMAqdC3RmIYNku2zGtJWjciMMGuvOEaphKA0ZH7XnUiroZOXdtBS6jC6T0liPiuMO6B4wGPTx8Yh1\ns+bB++8RH83Zuf4Mg9EG1iwxrqSqlnx47x6PjqcgYDTsc2d3C2c1y9WUqmmCXDzJMd6hjUbIiDxW\n5LFCimAoG6zbvjfQ8QeiKERSUiQKvCeKPjJRESKguTqOKI1lWVYYa9kejxj3C+bTIx48PuJcBIMM\nbWHr5Iin79xmcv0pHr35BlpDzwumQnBv/5Do4JhMKF58Zo8kidgZ95mXLQflEqEUDoVzDqVTnnvq\nFZ565jnyXhGs4oUjigTdXg8v4rDuUw394YDj0w84qL7Dz7z0ea5n8J0Hj9AqpRdL7p6vOFk31Ehq\n78n6BT2n+Pz2C+zlBdf7GfqJmgfT91g7x7vLhrKt0QdrthJ4fqPgL9zZoKkN7+0vmTf3efbKhKVO\nyYxBpD12evBMcs7Pf+4m33jkuH8eB0v7zgNROI9wApBsiTUvqgP88phJHNGqiJ2tAWkUs65qrm6P\nOa08Mw0fnK556eaQO3sTmmbJbHpEf2PC62++S2MNTz79Ct+695CtXs7OcJutkeKo0SQm5n1qrMgx\ntrPDE+HyDx1Bp3vwF6YxEkuM9oJ5YzhbaqyTmNawenDA9YFivV7wxT/3CsWkj17NOTs649W777Kc\nnfD1D8+4duU2k/aQ4s4Zb36r5ac++xyf+dIP8ej1+/zN/+MrvLNwNM5jHIyKmNoqmrZiGAWatHGe\nVeNoXYxzL9DYmmbtQmp3dIVIWmr/gKo6upSFh5gZ3/lLeCIp2EwjZnVL2VrOV3Ou7t1gYzzk3Yen\nfPD1dzisoPZQ5Am3Jj0eH5yybQo2Rgm2LTk5OeBksWJaG946WVC1R6T+AyYpfOrWNlujnDzxxBaM\nEMRKYZ0BD5NeRiJFwCw8FHFCbdqPfz3+G7rOv6fDeciSGKPN5UrHXxaF4G0gVcxoMGJ1fkovUYyH\nBWaYkYuU69eeY35yyO7mkJ5qUHGEaBfk+YCsn6Cd59bGNTwhO1C2ll4Sk6UxvVlJL42IpSRSMbUx\nCCnBaqTySCWxpkWJgCfgQh6B92HV6SUkyjGIDKZdEjnH7p7g996dMrOe+7MatV2QJTGpdvSyHG0N\nVzcnSO+omhVi5BkOU65n19ndOCOPG3SUMq9rtoZ93GrJdr/Hnes7PH5wwFYRs6oN460RBwczFvOH\nHO7u8eQwxzjNv3vD8szWhHcOW35dSrzoCoK8YDRKCl9xVVR84foWfSzvti3CQV6k3OhnyFjwzr37\nPPXcJ9jOC67sbDIuLMaMqMoZvWEf4Vp2b15lVSUMhmuGg4jD/SMGSUwepdwoEo6EpRSe5mLo7hRD\nQgUyVfcdCHgb1gXbsbo1OOvRHekniQS3N/uoQY88yULqtnJs7F3nJ2LFan6V7eG7nJ9PEVspg2SM\nvxnx8stPcWVvh1425lNfeYOT92bsZQWHbc0wT9jdUDgXXKO1MbQWjAWhUrxdoc0DyLfRpqWxDlmX\n+PaYomOzBgzqI5RfAsMkZWN7RLlYXgLnKopIkohBkVEMBjyxUbD0niJLeO72LZxZYtyMqLiJbudY\n50iSlFFfMK4Mi/MFSkS8c3TGi1cmFFlELC0NgiduDzh8VGNVjIshiRWJErSdoWwg+X386/EHoih4\nERyVkzT9rg1EuKtYXBDeKMl7B4ccrBsMinGR8Ykf+SSo0NKrJ59DeCiXU7IkmI8MJ8NgYJnEOCd5\neveTSO8Ch1+XVFXJIJ0xzhMGWYJNJKwN2gT35vV6TpEK9iLBThHz3rJh6QJLEqGIVI2Shhd3t9kZ\nKPra8e985s/x5qNjvvHufRoL0nmU9fzkS7eQUYTIUuIoZVXWGOupl8cYblHnMb084af/9L9PuahZ\nrGYkeUYxmnAt7RM7S7SqeNW+ynRxxq+99W1uXL9C3BP0hhN+79u/zc07/xlxMWHiY/ZuKz799IB/\ndihxwhMB1jksAuElRSqI0ohVJen3MziqqLQH6xkUGTvb29yYbOOlILm1TRzFLE/PKZenxN5izmdE\nFuYfPuD2k09x5bkdpK9Ru09g2oYPFw0REUXRY7VIMSikC5J24UO1d12Y8AWE7z1U2tIYz7pqUQJa\n51lqw41xwaevbVD0c/JEIb1CxQlxFnNjsIvTY67sDFgcnrA8eReR/DBf+NEx1z/xAjKOiTcjfvpn\nv8jh3/o1lqbmpVHEs7f6mFTT1C0+cqy1p/XdEsIuaatX6UUtrX4cTFJtINjlcbDlUzIYAeEFVrcI\nYFJMuLVR8Nr9Q54dp/Qjie6NiJ0nF4IvfeZZvvAKzJYBPI3yHJXGpHKMbhpis2BRleSp4jMvPoXz\nCZ+vG5xwxLGgl8RseIOzlncO7qEQzM4Mk42MxaohlVBkklGqgjmNC6G1/9YJouAj9Pki6QY+Yosp\nESxap1VNpiARIJwnjRRpkaHiBOGgXq1AG6QMK51ICiSOWHjS0QBvHcIKhDNUbY1CBXNWD4mSVFZi\nLZcrNt00OGtZxopJG3Tu5XyJwyOlJ40d/Txne9QjZc7e9lVuX/X8xlfOeHA2JRkolLScrhsSCXkU\nhdcbxSTGsVovQ7cSJ2RChfVhLEjHI/p5HxEbkApnDdo0mPMVEsNyWeKsZLVYcmUyIIoy+oMh88Vj\nxhvPUGQpWaaQFzbgIqSnXc7yHrRMqE2EylKuj/u8cXKKELBazxkVKbquSSMVePldeKsXjvAGgW0t\naRxTrUuwLWkEGAlKolyLdILtXkEjEywxxnbuTfjOZJZLpuol6AhhHerDZ9d4WFtB23puTPr0+z3i\nVKFUiAR05SL4bSYpQkAaZfQHffLiRbLBFpG3SCQyyfFpyuaNJ8hzRSEkwyLitGnxre44CqFDMZ0i\nT+BQUXDKbDrMK1cW70DZzqLO+5BR6S68GAWFdEzrMvhwqEBbTrIU1QXIZjIiijOyfITvDHadFMzP\nTkiSlCROEHiSKEYh6BUFqhiDcPSlIBWwmB8zWy86Do7HG4dpL9aogZGZRwopwmd1+V5/zOMHoijo\n5T45NdKntD7qbNbcZa6Gcg7poa7WqCji8Oic6d4m14qaBIHoR3ghqVZrdNMQSYXw0K5bpGiIBhPS\nzTHepbjyFNs2tOuKqq7QLiDdKomZnp5hjA4y6UiyLhvqFkRW8Lb2cPQhv/hP/ga99GWSCK6MDONe\nimSK8yU3d7e5+2DF17/zLT79xA2+du8RTduy1ob1qiYpBLLoXxKpjLFMBj3SOKzgnDO07QypEiK5\nQSb6yFjh6yWVXrDwD5nqBV873efUOlbLiizdxBtHMerBQDDo9RhHQ6LtLUSREN1zOCFCtJsgsPIQ\nzKJN3vNrXo5mjD2cHnzA/QeKP/MnfxSrLU1TMci3SIoMmSRI4dFVS7ko2dnZJRtOuOJixCwL2xHR\no1ZrtLEcN4ajUnOlX/BYp2ilsMYivQtsyyjCmS6BsTMeDQSgoEJ01hMJuN6LGHgwdcqqsciiIOkl\nID16eYYTLaqsUFkSCn4T1JveedoP3qY+PaX3kz+Fax22bXCrFvKEjZsFvcpQak2lwUlBqR2NDvmi\nQgaymnY2RA92o40BnOgKhw9FwRp7CT5L53G65rhuiZVk3lisUAwHBbq2tK0mUopEScZXnkTm1zj5\n4Cu4ckmv3yNJY9JEkSQF2jQICzjHYLRHLjJEs0LrKVW5wumWxkDVejaHPTye1mqWdcu8NvSziEFj\nMM50nJCPfz1+nCzJDPgdIO0e/0ve+78uhHgC+EVgA/hD4D/x3rdCiBT4X4AfBs6An/Hef/jHP4vn\n9MFXKIabRMOnIRpetpUXxirWtPTzjNo4Zqs1y7KiaXLSOEa2Gi8F8/UabyFOwkniXHDu8bMp/clG\nQN3bNdY0aG1pjMV4R5wkrOsl6/UaF7LOcc6jopAu5bxESM/xw8e06zX9pCBWDbk0xCpYqiVSMWs1\nJ6f7jHoZWSxpGo2xFis8q6qmUJKsbfHeU5Vrat1Sm5CRuGpKvPWUTYX2ll5hEHFOZFIwDVVVMltV\nlK3l1LWM0ghrWgZOYGPBuL9HtW7QcoG9soFwBmqBRF3SiIMwKmAKVgjm8Sb7dkDiW5648yJ377+H\nthrrFf3JLUwzQ6pAAXZSUtbBm0KqHk70ad2C1oOSQ5IoodI1TnoSbxhLxcbVGyz3NbUVJB2W4EXA\nEgJm9JGs+5LR6kNSdHdaEElBnkU8XlZoGwJunWmCTsWAS0qctaGj0gYIqczzsynCQvram8isx9J5\n3nv3fcraIvfXqJ5kUTYUaSARaR8EWLZzrfMu4BvOIPee+AAAIABJREFUXRjY+stJxzl/SbQSIriE\nWWuxeI7qhjSWARuxYb4f9DIWbUVrHG3d4LXFPHqAse9x7+6b9IdbZMMhkQKVeIRxeK2pnGO1XoPx\nuHxARoRua6x1GEeXcCZIIkmt/aXZdciWhCKWOBuxNBb7PYAKH6dTaIAveu9XXfr07wohfgP4L4H/\nznv/i0KI/xH4z4G/3f059d7fEUL8LPDfAD/zx74IlaNY46WirU9QyqKyyaUK0VuP1S2RCFz8eweH\n3Dvd41Y/prWWgW0RsaQo+kzPF7z6zXdIE0mcK/IsJit6rE4eYdtgRWadZ9U01Da4JWX9AR8evo+I\nM1Sag2mIC8XB+RucL36E24MtImV5fPwaTw02OFUOKyyuXVFPayqtMZHnq4/fwrhjeq7gnfcfcla2\nnBmHM5qjVYlwFhspRCQv/fTeubvPo4cnPP3kbSa7m9y8skmrLV/+zX/FaDzi+pPXka6lrVvOlkvW\nrUY7R5EoTlvD2+8dcuX2NleefYl3D1+j1Rnb8ZD68SmL2hCrH8KKIEQSnYQ6SiSmajnWGb8Tv8h5\nccS/+Pr/xEvP3eGs1cRtxP7Dt5gMx2TeIpuWetkwXbWULuPs4ATx4Yy6PeDxvXdoW9gZ9UkGEevW\n8QdnFa82A8y+wFnFQFiUqVFOYa3BkyOj0H144JLPpIJpjRQQS0mWqiCsXFgaqfj5v/3P+VNP7PBX\n/4sv4o2nbeZELgLrgrR+dUaU9ahNw+Szn6W3fZ3j199h/94DfuPL3+D9w3MmmcQZOJi2rLRlKCKk\ndIgoyBCsD6h94BaEK991pCUnOp9uC1H3/VAkQoFrXGAbCgeR8Ky1JYoVkRK0XtBYx+HhEbFULI/f\nprSGp56+gVUZd9+9R5akTMZ9lrOSRVkCnqRIsU3N49ZwZdRHesNiOaO2GmQo7j5OqWzFrGmpjcc1\njlRFFJFlVBSsm5oHbfP9Kwo+cFFX3V/j7j8PfBH4S933/x7wX3dF4c93XwP8EvA3hRDC/zFJtlEx\nxGhoygbfX4V5bh3R649QwmOdRbcNIHDWMS8r9qdLmhsjkrZCNYJYpKg0YbAxIjUCgWZYDBDOoFAY\n7zBeY5sa6xxVW1M7h5eC2hpqY+hlGUQeYTxWhDvFvQdvsnXlBu7sbUS/YDjeBivwytFojZAW02ik\n1phqhdaClW44rZsuii6kD1WtZSUb0qpERhEyioliybWdTQSCpzdGFP0BfZFRujVPvfwsi+MT1ssZ\nqRRoE0xiQwSeI/FwWBputAarYipjiREszxdkA0NlBOdlCwVdpxA8IYTzHdVYUFUN+3JIJC3F1gSb\nT4iEoDWGVS3oFYHR9+jwlOmqYVY5tFCMBj2S/Ca1PifaeZI/+OA+NzZHvPLEU1TVIjBHLUS9EckK\nIhzKVVR1S7F1ldbIzsilozF12gFJCHz5bgt3JOQqXIlZkvJwusbjQCosFowjyhLwkmS0jTOGqN8n\n37qO6m1z9ceeRb7++2x+800W/Zqi8OHnvADnSaNgoe+6ufvCbMW4C28E0SUxiW6sVHhhLxzePxJ8\nEkA9gSB2QTdiLx7jHFka4YB11eCtZ7pYMdoeUfQKyhaeun2Vs+mck+MzrJOoOOLZnS2aOOF4NsV4\nw9HJMVkkKZsmbJTisD4/ODkn7ec0ree8boNcnNBlOS8pkpj4+72SFMGZ9JvAHeBvAR8AMx9sfgAe\nAde6r68BDwG890YIMQc2gdP/r9/vRMz2838B50Eb15FcHEqGVrJta07OTtDOopxjdO02bz0qmb+o\niJG4VlNOZ7TLln5vSJ73UCphuVpjdc2yXBH1PWmRkqQJbWOpnUE7OFuuOThf0yJQSJKyROKZbN3h\nz/7Zv8j1K3tESYrMf4g432J+/z6/+ltfQxhDKxtQmllzSiRBmRKcwFc1p40mdY6edxjvWdSGjV5K\nZS0OwdvvHyIN9LyklySs9xuiOEJliihTXLs1YvPaHlkEi+kCFyssiuNVzU4sGSlFJuBk0XAzSpmf\nnMDiDDsas17sMzsxHM1a+JSCztMPB14GsNEVKXGestKG99wmz3/pr1I9+ApneoVQloeNId6UTA+O\neP2tV9m5dhNExkYaszcZ0TaPUEBWDGjGEpGmfO3RXVrreDxvUXsvUmUb9Ayo1rKqLYONTZzMiFTw\neZCyKwryIusLGm1xBDzTuDDPF3EYMaLE8OTTV4OhbtPgXI6vNGIgQcVE/QztJeX+Pl/5+79IPW84\nP16zXK05r0rWVc35rGWcS0yiUElMrR3O0mWYhtHG+tD6NyZAjnTUb6cUzgrMpaXchZxE4KUk65K7\nVRrTaMNcOyoLGyL4cjTOkQzGxFLQG43YP5ryy//8W4zyETubY3p5wiDfJsJQ1Q3vPDjFKcfdw1O0\nVHzy+gQXReRRhsWzqBqckMzLmisq4uB0zRv3T9nuZxhvmLeaQRrW6Ln6/o4PeO8t8GkhxBj4ZeD5\nP+ph3Z9/1LP/v7oEIcRfAf4KwM7uHkCw9OIjrrb3wSfP2hC2GcUC4yPOp+eUixlnzXNksWa7nzNQ\niuOjxyxX59RaI5RHRp62rTG+ZmN4hbzXJ0oUVd2Q5j2aqqFqwoyvrUOtF8gkxeG58/Rz7GyMLx2X\nhFKMN64zLLbAfw0rBNZZnNDMmynDfITyCuMsjQtZ2cZ7Eh/GlaNVzZVRTuEhS2N2hxm6NNxKB+RF\njyujEVmWsjRr4n7GeGOI1TVVoxGxAmupdMPpIth/ezwbuSKJBGlsWB4cEqNJohLdLlnOV1RnHTlI\nfGS1rnCXuZoubFYRSlHnG8Rxj6qZU6eKs6ZltVqhUsWLz3wSAZSV52a/x0YS4fMxj0zwJxiPJ6gs\nYb88ZtU0eBRisElLBN4gRcRkaxMrJVo4VKS4wBcvccbOu+Dirmy9x3TnQxYFPUQSKZ66toWuK3zT\n4HAhO8P2w0i2WlMbzcHhPa5dGeE2BZOdluViQf/BKQcnlvurmiSOWBlLpILC1Xi6fBERtlMXYrwO\n6bYuBB+rNCXNcrb6BcK38Oq805d1/qICkiQJ1HxjKG3AtPqTAYtyStVqfLde7xUFcZyyWDny3oSn\nX3meUZqyWjdMZ3NiISm0ofGO9vExG6MBN25dwWvLYrEE59CrilYbrFKUZY32jrrWrNMEa+F43RIr\n1WEN3+eicHllez8TQvxfwOeAsRAi6rqF68B+97BHwA3gkQhuJCPg/I/4Xb8A/ALAM8+94K0Lec6i\nM/F01iE6ToCxjnK5BqdY1yWOCBvn/Pf/8P/kxz/1LD/xw08xyhP27lwBK7Bt8D/o5zmJTGiloRQt\nxhpOj6as1hXLumG9WnF1Y4v95TGxDSCVSDOG4w2+8KOvkCYC50zgw7vggyCjCB8N8Fjq5gDdNiRi\nQFnbMI4gGKQD5vU5K20ZKUHm4a2jGbN1zZ+8s0PPWnZ3+igPvva0ouU8qikSwe7T10mylLpao12g\npzZNy7rSfPWNe7x1XrKRKDIJNzeHvP1gn+akZCOJoe7hmyOuT/Y4OJ4hTkFFMlx4PqgQpRcgQhCd\nU2DwOCRiY4dsusHJ++9htaEfSx4enLC1OWFnoyBTkkylFCKjFZLWGlQMk0lGjWN5vuBgtmZVt8xv\nfAYzvoVvBQkRcdqBtV6QiCgkgHv3EaFGhLhcZAgMbrpUKEP4/IWUDIebbI0iPvuFzxGvDrDG0E7n\ntK6lftiQZhleKc5nhwxTRTYeoVeOg/0HHB2cc3B6Russu4OMaanJcsFeX9EIjxIKq7q9ZDdiCeXR\nBqra4hy01mHrCiEq5tmSNO4MVUItQ3RGMta7y2SxNJa88/CMdblmZ3uD1bzkZLXBTi8nR9KaUz75\n3E3qacns3QPW/SH5xoT++CrrxYzl4QH18oQvvfICcZ7hraHWltp5yqblfLVGRIp+Irh7dIatG4ZZ\nwnLVoK2jH0U4IfGxRLT6+1cUhBDbgO4KQg78OAE8/G3gPyJsIP5T4Fe7H/kn3d+/1v3/f/XH4QnQ\nvandSRJEymF8CDx5h8Vjy32c92RxTGtDO3xerfnq23d5+fkb5EnEME9QQuGzLtYtlrg4ZC76psYY\nx7qqqesWW9eMioyHyxXvH+yjrcY4QQHsbE0YTIZ4H9PqC6ApzLjOO7SZd6GowUw2EooWQywViZQs\ndEOiJMp2WYiEk+pwXXM6r/ESBr2IosjpbxYkkerGnoLecIjHo5o1IY7cM52vODw55968xEhFbR1X\n85g0kTx3e4+2brCEsSWOY957+4iz4ynKjjq0P9iaWXchPLvY6gQgzfhQ7BIhOClX1KslG4Mx/dih\nen1ya1FFyqTfZ7WqODpf0LYtxSgh3xpy8P5jZqXmZFUzKw3jvWdoiLqUJIKWRajL+Rzf2cTxUSrn\nxecuO7cs4x22G9qlkGjdkCcp/eEI5vdRkUAqhTEN1WJFVmSkw4LepE8ympAmGSa3XLl2ikp7VPWM\nZQnHy4rGWiKRYAjJZLEQVC6YoEob4jwCtuGJU9lxEwLvIJYe7S1Rp9XA+2AP14WBGmOQJrhne+uw\nxrHWjjyNWeI4eHhAfmuPXhozGF1FGEE0yvArg1/OMW2L9Qbb1KjmnNEwQ6YRxhucNTTGUGrH6apk\n1WrSSDEtS+Z1S9MajLFo4zA2bFQuGKTf15UksAf8PXGRZAr/wHv/60KIN4FfFEL8DeBbwN/pHv93\ngL8vhHif0CH87P//U3jwlo9oS50pqgecRQnN1u4mj+8fhRNMKiLv0Emf49ryS19+jc/cucZP/sgn\nGBQ5RdpHRVEg/bQ1bdkwXzfMlyVHhycMs5Q7d56gbQy//O3f54OHR4GA4hyn03NOvzHlv/prf40X\nPvlpnn/ueV584ZMoIYmTHl4JKiokniSLKUZjNpMt7GpOuzyj0iWNs8ytQ0jB1DhiwCBYa8c/eu+Q\n7VTxH37yBtd2FKP+iCwrGA3GxHGKLhsaXfPwYJ/z6YyzsznNOrgqF3lC7uDpQcETe1tsDCWztef1\nu4cku2MKq3Btwq24YpwVPJzPLpmhl7yPbi0pg5snxnhqL0hMyV4Pnrx9i3cOT/mXdw+4Me6xM9NM\nJxlXJ0O8T0nzlMHVDYx2NE3F6cGCB+clx2XFH56veOoTr1BmO1jjiAjM0DiKQh6kD6266JB82cVm\nSCnwPsiCaxvi3hsHzrqwGrSeqq7wdoxtWmTdouKYoiiQUczi7BFNu8Iu1vQGfTLTwG5I+x7fuIbr\n5Yy0oV1UpKsFD98+Y6ufsjFImTmDq2oGeUGtLeNBysYk59pun5dvDrHasLIRmhgdZSAillWNdZp/\n/I3fDHiC1V1+ZziV27ql6CfodYPwgjSFwVafdd3w+lfe5eu//zY/9xe/xKCXMxr0SdMI0ZdY40CB\nEjG5i0m3U6xtadsG66Fcr1iVFa89OmDdaq5eGZEnMatSc3A6ZaoFJ7VjURush56EyFpqa0J39jGP\nj7N9+A7w8h/x/bvAj/wR36+B//hjv4LwM1hrcRc764u0IMJdIpKSzZ0dDvZPcNrhogQVJYjlDIfk\nfFXxxv4Jnz445sr2NmqcIq0LJJzWsFo3HBzPWKzWTIYDbty6RpEVlOWcNz98RGPCcjqcnBbv4fjx\nQ86PDvnq//1bPP/Cyzz34ku88sorwWvBWyIZBaquNVSVxFZrhHDkWUxdOwZphGg1WoS4BQdEIiDC\n2nn+8P4JVdlQRDEur6GxSBWxKtesywUPHj5mUVYgFHubQ7CWK8saJSS9PCZRnmGR0uLRsee8rJjk\nE4Q2fHhe0zeSzwyG/Kpzl3wPdZF4LOjs4H0QB3lPz60YyoYsjbizNWFWa/7lOyfsTSKE3MC4EOI6\nlmN2b/ww+vSU2fnrzBYlx2XLo7M5T+xe48a1JzkTYbvQOhtARMnF3g7nBPby+QG+K5DGQ9UGINZ1\nWIwNpk4oqag9aB/h6wbpamSckqYj0uSEqq5YLlbMZkvco0Nu9F5g8fYjjs6WnJ8v2T+ccl42LIuM\njWsbNEZzvNAYZ2jLmiv9IZNRwe7GgJvXR1wdJCjV0tSGutFIpUmTBOcbNjfHJFnyryFlrlNEhvM5\nYGEArbGMeyMSaej3YnSWouuSh4+OuLa7yXg0JFaSJEvpIq1BgjGGZm0QJkIbg9aa6WrN+XROWTZE\nsSRG0Mti6kYSRyBrwHuSSBI7H8RdrSWN1fe9U/g3fnigadpwYQJSKS6C1a33KBGxs3uHWL6OU1Fw\nLXIWJQQWKFtN0evxrQ/usXt+zs3dK0QqwjlHVbesyorlck0kBXeeucNoNGR6tuDVx+csyhavYnA6\neDh0azKBAGvQuuWbX/sy3/76V/nV/33ErSefRWFJVUzev00/b1g/fg1JyyCJmdWGfpxyYzRg/+QM\njWNpPGsTWvfUQ+zhqGyxh1MSIRgWKUodYazD1BXSaqx09Iseg15KnMQ0dUsvjYmkYGfcY3tzSGUd\n4xsD+veOcC4Am7GDg+mUVEQcqAqpJM6FOPKL7ALhPc47nLOhFRYg5o+p9h+z3Y870U7OG2cVj+YL\n+quK2sfkfU/dKKL9u8zPp5zMa07KlgfTGSfrmp/+iZeIxlucGNlFwfnge9htPISQSBvMVi5ERKEF\nD+eB9Z7WX4CMoY4Y63AqxgkYbGwyX67I2xq1LhlcfwbhFb3+MIB9izXt6piD0yXzGO594wGzyrIo\ndSCIaUujLdeuDEmsoNGazXFOMsl5/pU77Dy5zdnjmmwiebB/QKFacB7brlHFgEE+Jkl6KONJbHOp\n17jwqrg4BGCasJgrte0aYY/wDpUIskyxfzonyxI2VwsGg1HQc8gEIRM8QVfhpcLGgul8wXy54nQ2\np2nbsEECkkgxGmfMFkvyPKVoLWntUF7SOkvrLNIGA195YWP2MY4fiKKgjWX/4BilJFksiOKEIi/w\n3lO3mtYKbt18njc3vsPjxw9xtsbZoKITwhFbxe1E8MNXd2md5fTkHIOg3+tTJBHXr+yw96ltsqKH\nShRtWfPf/uN/wat3H2GiBLwjjhRKdi2goIs/d3gkQlmkEpTlim9+43f59zZfYh0tEfYR/Uoy2RJM\netvkvsZEQ6ba8PB4wRe/9KN866vf5Gy1RjtLaaGxkAnB7UHOVhZRrlvqRUsRRxSxYm+jR9KPEXl8\nmaOpjSWSgm3XQynB9Ws7VN5gvEJoSPsZ1bqmtZadXs7daYlzDfdmU1xbY5xHe6iNQ7gQXp/RkEWS\nwjvU4jEPvvIr/NifeJFoOSMrUvr9nL/+H/wY53XDr337Td69f5fHhw/xTcNq2ZDmKV5INJ7dnTE/\n/8odNl3JWjcUmaSuKqzTaB3RuoiWlEipztbBIzq2qO9Yea1xGO9Ik4TWWqQD7yVKQi1iojhmVdX8\ng1/4u/yZz93ixrVr+NbhvKM3GhElMaZpSIRHygF/+OXXqGvBQekprWemPUUsaUvDw4Mlz94YICLF\ntGwZ5TGvv3PAW7/zAK3XTITmCy9f5/aTWyxOVkRFjsLTHJ+wbDT53oBy+tGc7v2FoWA4BKJLEhPU\nNkj+E7GB9JZeL0ELT55J+kXCajbl4OE+15+4RZ6EYGKHxLcVp29/yKOjB9T9EUkk6aWKuvXo8KSk\nvQjVM0gl2BqPELLl7vQc7RVJL0EsloGpqS2DfgLLj8dV+IEoCsYYzs+nxEnMoEhI0mBB7pyjbjVN\na3BddJs2GmMN1llEPMQ1a5x32K5zGPV6bOY5qJhev08aSSIlKXo5MlZoo3l8fMZ3PjxAO4mzLUrS\n+TBegJz+AkYKc68juAa5gCx/umg5lY5PZAl9JTkxiqVtqbBsb+5Qnx6zsg3Qcu3GLvUHD9h1jqWx\nzDQ0zrHZKygiz5U8IvKSJE5JYkU+SJCxxMtgzS6kIBGA8CSxIk6jQJsuawajgmq9Jo4kOoLWaWpV\noHoJy1XL9nDAPQwGsNaSOkckQ+RdLhRYzTv/9H9l/egNPr/tyQcj6sUZKglBOomF7V7Kjz11i1dZ\n8/6DI6y3bO1ssTXMUc4yGuY8f22Lcb+HwPLw4QfIa7tI74iDlQON1hAJtFNgBe5CF+87GrGUOBsI\nN2kcB/EVkrY14AWTXp9bt24SGcODhSbKtkJOgvUgVBA9SRUCedMEvyyJI4vIMnq1DuY6QtEKiIWH\nVqMagwayJMUPMv7u7x4SE/Fzn99CNCXjXDHZHbB7PcWceZKx4OiBo5yXyBa805eCve8+LnoGKQMr\nEymoWsNyXVM1lo1xj1ophnmOkoJ+LwMpufvmOwhvaKwiNkvGacGbr33I9af22BjlCAFHs/IjTMYH\nMyIReSIlO2v68AqMt/TyIWK9Dh3ZBYb0MY8fiKLQNjXH+4+Cky4GFSf0JxPiSIGzrOuG6ckhH959\ni6rRIRxESLyZA4J12aA1SOnIioi8l6FURJ4EpVmc5sRRjPOef/QbX+VXfv81WiexLigB6QqQo9tP\nCy5nQgAhAydBKEkapeQjy1UJfSGxPuJOpHhkwFzdZLBTs3HzCYp+j3uHJ9x6eodktMnyq3+AUopc\nCQaR4OuHSz5/e8Bg0kc2jiiLGPdzZCSRUUwyLBDSYeOE9dEJEsHe02OkzzmbrZk3KzYnI+bvzrjR\nz3iwrmid4+7ZGbdf/iEa41mcLdjteVrnmR6ecH5yxMNvf5Xq/JDV/j28bYhEaFW/uVJ889vv89JT\nu0QK8kwxVCnGGvrJBi9f+zwOQZIGFZ9tGtbzOat1ifBw8/qAt954xO72iN/4zf+NV1//Dl5q+qMR\no61r3Hn5x+mNdyAb0iCphKJxnsr7QByLFcpZBldu4YRkMtzg9u0rjLY3ePT2e5ydnbFalUwGE966\nd4a9tsX1vQShwg7I2xrnNCQOkSqyYcH8vEbbhsZJHjeGlYU7uxM8jmWlQ8z7pM/v/eGCP//sLZIh\nxE3LTm/Mg0czZgczrHHYasF4exvnFEmq2BYpdVtfuizBhfr7QvIv2B0MWNUVeZbwxO1r/Kmf/NN4\n53nra3/AerHClhW2aomvjNgdj9gY9WlqHYRY5Sn33nnAJ1+6w+7ztzk5m1HWDaumRe0p9GtLer0B\nprFkccqwMLgNSOMU/8EU5xzahNyMNsgnaVr3sa/HH4iigPeUTY23BmdrhIfp8SOa9RmttdRacXz4\nmLZpvkv0AR1zhLI1nKwqlIjBWCKpUFKGFZOMUGkKEppW80+/8RqLqsESVp2JUpe6/sBX6T5m8V33\nAdEBn0J1YpOQ0hxMnh2HxnPUNGwB07ni2p2YvBDMjxs2Tiuq+QwpPCsPyju0V8G8JS4QMmI+K0n7\nBic813bHECsoJFZIrFQYAU5KjBMoaZHC0uiWNI9Ji5hq2rCRpuiVR1jLQavZ3RAMkxFvfvk3aauS\nu9/4HdrVnLZc4l0XdyclIg7RcqVz/MrXX+Nzn/0kdnGOa1qKjQGxjdC6RniHt+BbDd6CNlgLbdVy\nfWeLXt6yaBY8/Yk/wU9d9Zyujtjff8xiesZyds7Rww9IiyE3n/8Mo62rbF5/HisiVlYxdxEmyZBe\n85d+7i+znK+DjX4PvG54YB20miJJUdsZR8uK4nTOld0RkfQgHFIYvG0gsiSZIkugyRTjYUbmIupa\nMPC2o4l7ZrVFSodsDGRwqJc8cfNpcud58HgfRMJwIljOV0x2r6Ol4+TogHxjK6SFiy7LkYs074tz\nOQC6SoWz58eef5LPf+7/ae/cYuS6qjT8rXOrU7e+252OQxzHxCMyCSRWyAQIJBEIiCXkRMxDHhDS\nCAkJGAke5iEjEIJHkGY0mgdAQcNIIMQtIBQuEYRLFAVywcnYjsGx4zhO7HTH7mt11ak617152Lvt\nbtPtGNJd3SPOL5XOqV0lrb9WnVq19zprr//tDA6Po7KUalglj1PTkdlx0Np0JA8qAY6YGc9cp6A6\n4DG6cxzXtqBX2qhC1UMfxzEyilleUCQelYpHmPk0CqgHDnFekMYJRtDXLHPavXWsU+gHlFJE7UWE\nAq1zkyBst+h1Y+bn5+h1I5I0RjsODqbQRWyGWjtCKiGPnzjLP991C4VT4LoOge8R1Gu4rovjORw5\nfJgHfvZ75mIoxEHpFN/1z3f/WT4RvJAZl2VNWk2WXkS4xodUa2r4xK5PS8zdg5mZaXbuHOf/fv4s\n1+65mpdm2pxtR/hZhvI8VJrbHXgFe0Z9XosS9u7ZyUijzUizwqtnz/BaXCFrpXiLHo1KxegA6gJX\na/J2Bk1NFifQTTh54DTVK0IGRrejjs/y0uQM455P7+WXiedDur2Ag8/8AqUKU6LkurgOKDH3+D3X\nM3kUcRDP4cTcPI8cPcO+O27DdzPqgUb3eqh4kUxn4Hl4jhHryV1Bcp+G24S8zZnne3ihItIRd91+\nF535Dv/1lf81W4sdgSQmTRKO/O4nFwRpEPz6EGO7b2Z0540ozyFe2IZLBUTIWznddoTqRIzUq0RJ\nymKsaAxVObbQ5s3tiMFmCHlM0u3SqS8QtwKGRkfZg8fC0Bzp8UnyXka71cUNXMKRAerbavzDrRP4\ntQoHfnSOt9x0A7fvniBxavzpxBmm2zXSLOXobIcqAWOuJm4tsmPbOIuJ0E1jQrfAd0xRmLYbuxzH\nw3OFSsVjoRtz444xPvWJjxDUGrjVJnnSoxH4+IMDqCQB18Gv+TgOhM0BBIcgDInzFmm+gDvcROuM\nLEtIej08cl45MEst8Kl4QhxFTE91GB0aJKhUGailXD0+QPvMAp2sB7iIa8R/1SpyfWthSwQFgCSN\nTQkupvNRL+4SJz3iuEdeZOcj3tJq36gTu3aXnbAYd5lq9ZgYDBjzXFxfEJUhUqBUwVd++jivzHaI\nlVmSBK5vfqAalLb7+sHuYLHbY8Xs+T+v0LxU9ONoAgVTcUxKTOTmTBYp24Mmp89Ok4nm1ddmaFSE\nVzsJnhVTDR2TgDLZC6ODsOttN3Lyyd8zUvfxr7yKTpwRdzUqydBhxRTHKNNM1QuMCIjv5HgqQfKE\nxVM95rMWRS9nohLQTgoac9O0ZzW9zH4em/T5GKovAAAHc0lEQVRCa8Q1QjaOkcMyXaSAohBEXB78\n+a8IHcX77nwHowMNJKzRSNrknQWyKMMNApPkdQRP54guiLo98FO8sErFG0B0zg17dtAIfaJubIu+\n7G4BMfsUityQSxfOER/6De3J49AcJvjwR1H4xL0UyVOKpAvarM0bfoWpmVnu3X8Ph574HYtRTLVe\nQcc9Op0ueVCjFg5QCUJ6YUKQVHGKHCfXjARw5XUhQ7vqLETCzFSPXFLaKqOWadIgZCHqcOr0STpx\nTsUTetrlmobPqbkIV4MXpYjWZEFOMwDzp7GsEEtMHirPcnYM1vjw+99B2KxTZG3EXndOrY52PKTI\nEMcsW52KUS13HPMjrtaHcL06meOgMkVa5GQqJu4soouEZqWK5zoE4lAkOTgOnu+htOaq7U1Oz0TM\nzpnaFiNVss79FPoBrQpUmqEdSNOYLI3pdlqkaUGh1fmKQmSpK7ARUUXMhhlE8Coh//Prp7hmfJib\ndl/FnrEm49tHmJya5hfPPM+fzrZRSlGo3PR8xLMVlBpspDe37qzEGaaSEVsrobXGc01BzOEoYgiH\nyVbH1F2GRs5LDzU5dfIUeVrwcmeOWGsipWiIw/BVE8ydniTwfUSgVcDsfA98B8+FbichCDzG6hWG\nq77RAszMHRZHgVMI8XwXrRRFXlD3h+hFuVFwTgp0pkALvih6ifBPN9zAE8dfADcySdLzqyJzogt9\n/lalIxqVmcz0rFI88ONHePK55/ncxz/ExPYJqhM7KKJBoqnXjEKbFSepNweJ04wo07QS4eo37Wao\neQWtdsZPHn2SblbYKTKAurBEK1KWRKgQ0ykomp1EFqbRSUESzdPu9nCdjEIn+M0ajsrJugX3feST\nNCcibt//bl5+7DBz8z3i6RZ+bZABBqmPDqDynHpagBKu/8e38NJkh4eeOEbrlGJ3bMrZzx2fYard\nY8foEO7sOX77+AydmQVCgQGE5tA4Ohzmipuv48WHH8bXQqbrjF0xTPf0KxSJoFwzjRcxupOFUogW\nXFz+5X13cOcdd5IlGeSYmZbKCbRJqma+S70+QK3q47kunhsYEV5zv4s9E2+m4hbEcUGaFyjtM9gY\n5l3vvJKjRyYRRwh9n4YLoS7Ii4IsLxiqVxgaqlPpZGSFIlfaLKVFgMtbQsjrVSD3AyIyDURcYidl\nHzHG5vPYChyg5HEx/r/z2Km13vZ6b9oSQQFARA5orW8peWwNDiWPv18eW0N1ukSJElsGZVAoUaLE\nCmyloPDAZhOw2Ao8tgIHKHlcjL8LHlsmp1CiRImtga00UyhRosQWwKYHBRH5oIgcE5ETInJ/n22f\nEpHnROSgiBywYyMi8oiIvGCPwxtg9xsick5EjiwbW9WuGPy39c9hEdm7wTy+ICKvWp8cFJF9y177\nd8vjmIh8YB15vElEfisiR0XkjyLyaTveV59cgkdffSIioYg8LSKHLI8v2vFdIvKU9cf3RCSw4xX7\n/IR9/Zo3RGCp2cZmPDAVyy8C1wIBcAi4vo/2TwFjF419Gbjfnt8PfGkD7L4H2AsceT27wD7gYUz5\n0W3AUxvM4wvAv63y3uvt91MBdtnvzV0nHhPAXnveBI5be331ySV49NUn9nM17LkPPGU/5/eB++z4\n14BP2PNPAl+z5/cB33sj9jd7pnArcEJrfVJrnWL6Pe7fZE77MToW2OM9621Aa/0Yf9nMdi27+4Fv\naoMnMQ1zJzaQx1rYD3xXa51orV8CTrBK562/kceU1vpZe94GjmKkAvrqk0vwWAsb4hP7udbSWnnQ\njl/sjyU/PQi8V+Sv0Zleic0OCuc1IiyW60f0Axr4pYg8I6blPMC41noKzEUCbO8Tl7XsboaP/tVO\ny7+xbPnUFx526nsz5t9x03xyEQ/os09ExBWRg8A54BH+Cq0VYElr5W/CZgeF1aJZP2+HvEtrvRe4\nG/iUiLynj7YvF/320VeB3cBNwBTwH/3iISIN4IfAZ7TWi5d660ZyWYVH332itS601jdh5BNuZR20\nVi4Xmx0UljQilrBcP2LDobWetMdzGJGbW4GzS1NRezzXJzpr2e2rj7TWZ+0FqYCvc2E6vKE8xOiU\n/hD4ttb6R3a47z5Zjcdm+cTaXgAeZZnWyiq2zvOQS2itXC42Oyj8AbjOZlUDTJLkoX4YFpG6iDSX\nzoH3A0e4oFsBK/UsNhpr2X0I+KjNuN8GtJam1BuBi9bm92J8ssTjPpvp3gVcBzy9TjYFIw1wVGv9\nn8te6qtP1uLRb5+IyDYxamzIBa2Vo1zQWoHVtVbgcrVWLoX1yNq+wUzrPkyW90Xgs320ey0mc3wI\n+OOSbcxa7NfAC/Y4sgG2v4OZhmaYKP+xtexipoZL+p3PAbdsMI9vWTuH7cU2sez9n7U8jgF3ryOP\n2zHT3cPAQfvY12+fXIJHX30CvBWjpXIYE4A+v+yafRqT0PwBULHjoX1+wr5+7RuxX1Y0lihRYgU2\ne/lQokSJLYYyKJQoUWIFyqBQokSJFSiDQokSJVagDAolSpRYgTIolChRYgXKoFCiRIkVKINCiRIl\nVuDPRzNaohQA2u4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb1387214e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "img = get_sample_image(G, n_noise, n_samples=25)\n",
    "imshow(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fb138329780>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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qgKBAHnOWd7DJoj5nFu5q60KXuPqkYVLpDmIfdx2L6sf2tr7mPo0jC8uqTmhc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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb1383a1a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Fake Image\n",
    "idx = [3, 3]\n",
    "row, col = IMAGE_DIM[0]*idx[0], IMAGE_DIM[1]*idx[1]\n",
    "imshow(img[row:row+IMAGE_DIM[0], col:col+IMAGE_DIM[1], :])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fb138285c88>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ctX3g3C+bKMeoJS7ICkRv9owqWAWX6Wio3W0xmKZGfR1diIQjfTBes1VH4Bnu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\nGyngwhK6AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb1383d65c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Real Image\n",
    "i = 14\n",
    "rimg = tensor2img(images[i])\n",
    "imshow(rimg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Interpolation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def sample_noise(size=None):\n",
    "    z = np.random.random(size=size)*2-1\n",
    "    return z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([10, 64])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z_a, z_b = sample_noise(n_noise), sample_noise(n_noise)\n",
    "zs = torch.tensor([np.linspace(z_a[i], z_b[i], num=10) for i in range(n_noise)], dtype=torch.float32).to(DEVICE)\n",
    "zs = torch.transpose(zs, 0, 1)\n",
    "zs.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/yangyangii/anaconda3/lib/python3.6/site-packages/torch/nn/modules/upsampling.py:129: UserWarning: nn.Upsample is deprecated. Use nn.functional.interpolate instead.\n",
      "  warnings.warn(\"nn.{} is deprecated. Use nn.functional.interpolate instead.\".format(self.name))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "torch.Size([3, 64, 640])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imgs = G(zs).detach()\n",
    "imgs = torch.cat([imgs[i] for i in range(10)], dim=-1)\n",
    "imgs.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fb1381eb470>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ZdS6bfC1+XbtOG91EZH4Y8m7VlNDqGC/E+Xa0kVkVOhwdk410NTK9F4xVV8WY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8tZDflVWoBMGdqKu/J+daeyXlIHI4axp1XqW0pywqJU1MuNYLBrmyqsGczpSsx2PKu5fW4fTS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\nuk6Qh7e+i/s5Mj0E2BwF+7s5VfZIaJ+t7XGYbABO7oY2UkZaW88xpE3e3tz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3i7r3yls5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\nJCQkJCQkJLwJ8P8Bu5UFHD37QE0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb1381f95c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(20, 3))\n",
    "imshow(tensor2img(imgs))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
